Feature Corner- Transforming the Digital Banking Landscape

Digital technologies have been helping banks and financial institutions to seize new markets, grow the business, and cut costs by providing a competitive advantage.  We see tremendous changes in the banking and financial services industry impact businesses and in an attempt to seek insights from business leaders from the industry, our Co-Founder and SVP Mr. Sridharan J. S has a conversation with Mr. Palani Balasubramanyam Nama, Head of Open Innovation and Digital Transformation, Societe Generale, who has over 21 years of experience working with Innovation & IT divisions of Investment Banks, Financial Institutions and European/American Multinationals, about the state of AI and other technologies in banking, in our exclusive ‘Feature Corner’.  As the Head of Trade Execution, Palani is also accountable for the IT & OPS team ensuring on-time and secure trade execution across global exchanges, efficiently from India.

Sridharan – Being in the banking industry for more than a decade, tell us how banks fostered innovation before COVID and how the perspective has changed during this situation? 

Palani – Pre COVID-19, innovation in banking was more focused on internal optimization, focused increasing digital footprint for efficiency, or improving customer journeys.

COVID has compelled to accelerate the adoption of contactless service delivery for customers, at a rapid pace. This constraint forced banks to partner with external eco-system for rapid transformation, to provide neo banks equivalent digital experience for customers. Apart from service channels, there is quite a bit of investment being done on employee experience, like enabling them to work from home, across multiple secure digital channels and collaboration tools.

Sridharan – Approximately what percentage of spend, do you know of, that banks spend on IT costs annually? 

Palani – Large banks spend between 5 to 10%, small & medium banks spend between 20 to 30% of their total spend on IT costs.

Sridharan – Approximately What percentage of banks’ transactions or what areas are automated?

Palani – Above 70% of P2P transactions are automated, however, B2B transactions hover between 30 to 40%. B2B transaction STP % is catching up in the recent past with the evolution of Machine Learning & Smart Automation.

Sridharan – As more devices connect to the internet and the concept of the Internet of Things gaining rapid momentum, what are the preparations banks are involved in the rapid increase in equipping themselves with the huge amount of data generated from the devices?

Palani – BFSI has been slow in the adoption of IoT before COVID, given security threats and central bank regulations. However, post-pandemic, banks are more open to adopting digital channels to deliver contactless & remote client experience.

This unprecedented disruption has increased data volumes multifold. Most of the banks have established internal data analytics teams to address basic data processing needs. Despite interest in partnering with external partners, there are roadblocks to be addressed before it can become reality, especially around secure data sharing, multiparty computation & adherence to regulatory norms (GDPR).

Sridharan – Data, being the most important factor for the digital transformation for banks, what technologies are used to leverage data to meaningful insights for smarter decisions?

Palani – Large BFSI enterprises have multiple tech stacks based on needs. However, they have well defined IS strategies guiding the usage of tech stacks based on problem patterns being addressed.

While exploring partnerships with tech giants like ORACLE, Teradata, or IBM, there is an intrinsic push to adopt opensource tech stacks, to ensure open standards and cost optimization.

Sridharan – Do you think banks are struggling with the rapidly changing technological landscape in terms of which technologies to adopt and which ones not to? 

Palani – It is a challenge to keep up with fast-paced Technology landscape evolution for any organization. It is more complex in BFSI given constraints around tech choices to be made keeping in mind the security and reliability of services. In BFSI, business capabilities and security take precedence over technology grandeur. 

There are few instances of large banks partnering with external partners to disruptive technology transformation, however, results so far haven’t been very encouraging. BFSI transformations demand as business acumen and technology expertise. External partners tend to be good at technology expertise, however most of the time they lack business acumen.

Sridharan – AI has been long seen as the next big thing in the banking and financial industry be it in the area of planning, cost-saving, customer experience, fraud prevention, or anti-money laundering.  What do you see as the future of banking given the exponential growth in IoT devices, cloud computing with Artificial intelligence capabilities?

Palani – AI, so far has been capable of helping in raising proactive alerts, simplifying decision making, and processing of large datasets. however, it is far from autonomous process execution or decision making, which limits the full-scale unsupervised deployment of AI-driven process flows, which in turn would help increase the straight-through processing rate.

Necessity is the mother of invention. Pandemic has increased BFSI’s appetite to adopt technologies like IoT, Cloud computing, and AI. The exponential growth of these technologies should be complemented by the rapid hardening of enterprise cybersecurity strategies, without which, banks may be hesitant to leverage the full potential of emerging technologies.

7 questions Contact Centre Data Analysis can answer

 Industry Watch Corner

The lack of access to physical stores due to the pandemic has increased the traffic for contact centres worldwide. Contact centres are bridging the gap to communicate with customers, emergency helplines, on-call services, and inquiries. Even before contact centre agents were required to work from remote locations, Cisco’s 2020 survey revealed that 62% of decision-makers plan to implement a cloud contact centre in the coming year. 93% agree that technology is very important in creating a better customer experience. Contact centres are a treasure of data waiting to be discovered. They have data acquired through daily processes like telesales, telemarketing, customer surveys, collections by means of outbound services and customer services, emergency response past inbound services. Analyzing contact centre data can help not only the customers but the contact centre decision-makers as well, to improve service and increase efficiency. Artificial Intelligence-based software improves the customer experience by monitoring key phrases and then prompting the agent in giving proactive responses. They can identify challenging calls for agents to handle and route the calls depending on complexity. AI is also gaining popularity through chatbots and solving issues without the need for agent interference. Chatbots need to understand the tone of the customer and respond accordingly. A healthy Contact Centre must conserve excellent hardware and software solutions providing a direct impact on quality assurance/success metrics, call statistics, customer implementation, and proficient Contact Centre agents. Contact centres today can answer more than just calls. Let us look at what other questions can be unraveled through analyzing contact centre data.

1. Is the Customer Satisfied?

According to a 2019 survey by Deloitte Digital, Customer experience or CX is a high priority (57%) among the surveyed centers. Harvard Business Review found in a study that a focus on increasing (Customer Satisfaction) CSAT will help businesses retain 74% of customers for another year leading to a substantial increase in revenue. We can increase customer lifetime value by looking after the key determinants like customer acquisition, customer retention, and customer margin. IVR (Interactive Voice Recording) Systems have been in use recently to reduce the number of calls reaching an actual agent. Valuable insights on the reason for the call, the caller, Opt-out Rate, self-Services rates, and incorrect routes can be evaluated through IVR data.

Representative customer satisfaction metrics from FLASH, the analytical dashboard

Representative customer satisfaction metrics from FLASH, the analytical dashboard

2. Is your Product facing a regular or common problem?

Collating and categorizing issues is a great way to understand the product. A repeated complaint of the product needs to be fixed for future versions so that customers do not engage in expressing distress and reducing brand value. Categorizing these issues also helps in creating a common answer pool for contact centre employees. AI can be used to direct the calls to Subject Matter Experts to handle issues. This will reduce escalations and help employees with better ways to tackle problems. We can also perform sentiment mining to analyze the sentiment of the customers on service level parameters such as resolution effectiveness, feature variety, etc. by leveraging social media sentiments of the public on various products or services of clients for improved business services.

3. Is your Contact Centre Efficient?

First call resolution (FCR) is an important metric that measures the efficiency of the contact centre to solve issues in the first call. A long waiting time is another factor for low customer satisfaction. Measuring the average delay of calls or the call abandon rate or the response time will give us an idea about the need for more staffing to handle the surge in calls. Response time metrics and call resolution metrics are indicators of efficiency(image below depicts metrics from an AI-driven dashboard, FLASH). Customer Effort Analysis can be performed to measure the efforts spent by customers to get various services rendered through various touchpoints. Customer journey analysis can be used to identify the pain points that customers face while traversing channels for a specific purpose. Path analysis can help identify the patterns in the menu/caller path in a time slot. All metric dimensions can then be evaluated to arrive at actionable insights about the customer segment in qualifying dominant paths, dependent paths, and average time spent on the path, etc.

Flash insights

Flash analytics

Flash dashboard

 

4. Is your Employee productive?

Average Sales per Agent is the measure of sales that an agent closes within a time period. We can use this metric to award agents performing well and provide targets for employees to work towards. An agent’s Utilization and Average Handle Time are two primary contact centre metrics to focus on when trying to measure an agent’s productivity. We must also be careful of the escalations that an agent faces and train employees who need to improve performance.

Flash AI board

5. How many Customers at risk of attrition?

According to contactcenterpipeline.com, Attrition is considered the No. 1 challenge 27% (up from 19.2%) for contact centres in 2020. Measuring the Net Promoter Score or NPS of customers helps us to plan a better approach for managing customers who are at risk of attrition. Predicting churn rate qualifies the steady-state level of customers at any point in the network.

6. Are there any opportunities for Upsell and cross-sell?

Affinity analysis/association rules of mining are used to analyze the co-occurrence of relationships among activities performed by customers and discover the cross-sell/up-sell opportunities accordingly. Recommendation Systems help identify products customers need and help contact centres create personalized offers based on purchase history and products commonly bought together. Use of Uplift Modelling can be used to identify the right set of customers(the persuadable) to be targeted for marketing campaigns.

7. Can your competitor be a reason for low sales?

As contact centres are a hub of engagement, customers often give honest feedback on products. You also find that many potential clients are already using your competitor products. Employee engagement plays an important role in analyzing these potential risks to the business. Predicting the customer churn rate helps to identify the customers who are likely to leave the network.

Contact Centres are now moving into a space of self-service where customers are adapting to emerging artificial intelligence software and chatbots. Customers today do not like waiting for calls to be answered, they would rather finish queries through chatbots that do not consume time. IVR systems are slowly being replaced by intelligent assistants that provide human-like interactions. Having an advanced chatbot and analyzing its response can help you understand your customers. With these forms of service, direct calling and email, data is being collected from multiple sources. Decision-makers need regular reports to keep track of their performance. While automating this data is now a necessity, the use of AI to analyze this data is a competitive advantage. AI-based dashboards give you the insights needed to understand your data. They highlight possible improvements and forecast staffing depending on query traffic. They group dissatisfaction and anger sentiments and analyze agents who deal better with these emotions. Customer sentiments change with time, people are more impatient and now more sensitive due to long hours indoors. Many factors can be associated with good or bad performance and identifying these factors can be tricky for decision-makers. AI learning, analyses your data in real-time and helps you get a holistic view of your business. Analyzing and processing this data turns your contact center into an answering hub. Companies that ignore customer service face an inevitable fate of resentment and destroyed brand image. FLASH, a product by geniSIGHTS ensures your contact centre does more than just calls. FLASH is a one of a kind voice-powered AI tool that gives your contact centre the power to stay ahead of the competition and give highly efficient results. Our solution uses advanced analytical techniques through various statistical models and machine learning algorithms to qualify underlying patterns and mine hidden insights from data. It is lightweight and cloud-based, facilitating analysis at your fingertips even as you sit at home during this lockdown. Contact us at https://flash.genisights.com/ for a demo today.

Effective Decision Making

  FLASH CORNER

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The BI Survey research found that 58% of the surveyed companies relied more than half of their decisions on gut feel or experience over data. But two-thirds believe that information will be valued for decision making in the future. Highly data-driven organizations are 3X more likely to report significant improvement in decision-making, according to a survey by PWC.

The HiPPO effect, coined by Avinash Kaushik and reiterated by Bernard Marr in Forbes, is usually the most experienced, most powerful, or highest paid in the room. Once they voice their opinion, others find it disrespectful to voice their own. This may not lead to desired results. Let’s think if the decision would go on a similar path if the time and resources would be different. Given more time, you would probably rely on the opinion of a board of directors or your experienced colleagues. Will you still be completely confident?

   Data however changes this. You have an unbiased opinion based on facts that leads to better results. Data analytics uses information from all sources and simplifies it to visual representations that highlight what you need. You are now capable of viewing all your information in just one screen. But data is useless until interpreted. Data-driven decision making or DDMM lies in interpreting this data for actionable insights. A term fondly called as data storytelling is considered an art. Artificial intelligence-based complex algorithms make sure to see more than what the human eye and mind are capable of. These insights help drive your decisions in directions that lead to success. So you have got your data, got your insights from this data, why then have you not used it to make the decision given to you 10 minutes ago?

The problem with many AI solutions is the inability to get exactly what you want when you want. You have a ton of data and a ton of insights but how does your BI tool know what you need to reach the answer for the question given to you 10 minutes ago. You have knocked on the door of data analytics and are seeking the solution. But can’t you just ask?

FLASH, by geniSIGHTS, gives you the power to simply talk to your BI tool and seek the information you need to come to the decisions that will open the right doors. In turn, FLASH also talks back to you with insights. FLASH is a one of a kind voice-powered dashboard that provides quick insights into your organization’s metrics. It goes beyond the functionality of Business Intelligence and uses conversational AI in the dashboard to respond to queries and generates real-time insights for actionable decision-making.

FLASH is AI-driven, lightweight, and easy to use.  It can easily be integrated with your existing data systems without having any extra software. It is also accessible from any location and insights can be exported and shared with the members of your organization. The visualization can be customizable and easily understood without any expert knowledge of data. FLASH ensures that your entire team is on the same page, making your user experience smooth and without any delay. Unlike other solutions, FLASH learns continuously to understand what you need for your business and becomes your virtual data assistant over a period of time.

FLASH is your AI agent who has all the answers you need to make decisions in a FLASH. We have seen how Amazon’s Alexa has made life easier. Shouldn’t your dashboard be able to do that? Now you can make your decisions within 10 minutes and be sure of it.

FLASH gets you what you need, you simply need to ask it. Check out FLASH at https://flash.genisights.com/

12 KPIs to measure if you are in the Manufacturing and Automobile industry

 Industry Watch Corner

You can’t improve what you don’t measure. KPI or Key Performance Indicators are the metrics used to measure how your company is performing. If you are part of the business world, you are probably familiar with the term. It is important for every business to choose the right metrics that can measure the success of your company and work towards improving your performance. It is important to understand that KPIs vary with organization, department, and even team. There are many types of KPIs. They can be quantitative, measured as a number or qualitative, based on standards set by expert opinion. They can be leading, predictive of future success and failure or they can be lagging, reflective of success and failure of past events. But the best way to categorize KPIs is by industry.

Just like any other industry, KPIs are important to measure the health and progress of the Manufacturing and Automobile industries. Besides knowing what your KPIs are, you will need to work on ways to improve these KPIs. KPI real-time dashboards monitor your KPIs and give you insights to improve them. They help in maintaining your supply chain and manage your logistics by retrieving data of your systems in real-time. By using Artificial Intelligence they run complex algorithms of predictive analysis to forecast demand of your vehicles. They analyze your customer data and give you insights at the right time to increase and decrease production depending on the demand of the customers. They help in customer segmentation and analysis of regions to convey high performing regions. These regions will need higher demand than other low performing regions.

Let us look at key performance indicators examples for the Manufacturing and Automobile industry

  1. Overall Equipment Efficiency: It is the product of availability, performance, and quality of equipment. It measures the overall effectiveness of equipment and can even be calculated for the factory. It highlights poor performance and quantifies improvements.

  2. Production Volume: No. of units manufactured in a time period. This helps us determine if the supply meets the demand of vehicles

  3. Production Downtime: Analysing your planned downtime and optimizing it for better productivity.

  4. Utilization Rate: Measuring the capacity of production against the actual production. The utilization rate will vary according to demand during the year but low utilization for long periods may indicate losses.

  5. Cycle Time: The average time required for making a single unit of a vehicle. Recording data of the production line will help us determine if optimal time is maintained in each process and point processes that take longer. We can then optimize production time.

  6. Yield: Measure of the number of vehicles that are made without any defects in one attempt. We can measure the yield for individual processes to highlight faults in the production line.

  7. Recall Rates: It is the percentage of vehicles or parts that were sent back due to defects amongst total vehicles produced.

  8. Inventory Turnover Ratio: This is a measure of the number of vehicles sold and replaced in a time period. It is a ratio of vehicles sold and vehicles in the inventory.

  9. On-time Delivery Rate: The percentage of time that a manufacturer delivers a product on time to the customers.

  10. Back order rate: The number of orders that cannot be fulfilled when the customer places an order.

  11.  Mean time between failures (MTBF): It is the average time between failures of a system. It can be calculated by dividing the total operation hours by the number of failures during this time.

  12. Mean time to repair (MTTR): It is the average time to repair failure and return to production. A good MTTR will tell you your readiness in emergency situations and how your response is to failure.

FLASH, a product by geniSIGHTS is a real-time dashboard that can help the automobile and manufacturing industries by assisting in supply chain management. Its voice-controlled AI model solves your queries on the go. You can monitor the data across regions in a single view. The simplified visual representation will help you identify red flags quickly so you can act on them and improve your manufacturing process. Using a KPI dashboard will not only assist you to monitor your KPIs but will give you the power to optimize them.

Read about flash at https://flash.genisights.com/

Harnessing the power of data analytics and AI with cloud computing

Today’s businesses are reinventing themselves with Artificial intelligence capabilities, be it chatbot or robotic process automation.  The increase in AI-enabled services are also creating higher demand for high-performance computing to process large gigabytes of data. This has also led to Big data processing using cloud environments.  Virtual assistants like Alexa, Google Assistant, and more have brought cloud, analytics, and AI a connected experience in our daily lives. 

Market Size:

According to Infotronic, the Cloud Computing Service industry is predicted to reach a revenue of $150 billion by 2020. They also predict that 83% of enterprise workloads are going to be on the cloud by this year without considering the pandemic and how it has now forced many companies to move to cloud solutions. Data analytics in the cloud will help your business be a part of the 90% of companies that use the cloud in some or the other form.

What is cloud computing?

Simply put Cloud Computing is like renting a house instead of buying a house. You pay for the time you stay without having to have the additional responsibilities of maintenance and up-gradation. Without physically downloading the software you can use the internet to remotely access everything.  Everything you need is located across multiple data centers having dedicated servers, data warehouses, and multiple backup data centers to make sure your data is safe.

Essentially there are 3 types of cloud deployment

  • Private cloud: A private cloud is a cloud environment dedicated to one organization.

  • Public cloud: A public cloud is a service shared by multiple organizations using “multi-tenancy”, where virtual machines are used for renting the same server space among multiple tenants.

  • Hybrid cloud: They are a combination of public and private clouds. In Hybrid clouds, companies use a private cloud for some confidential services and public cloud for other services.

Cloud computing can be categories into 4 types:  

  • Infrastructure as a Service (IaaS) – where you can rent out the entire virtual infrastructure like servers and data storage spaces;

  • Platform as a Service (PaaS) – where you can rent platforms to build your own applications;

  • Software as a Service (SaaS) – where you as a business can access developed applications that run on the cloud and

  • Data as a Service (DaaS) – where your entire business data is on the cloud available for remote access

Can we analyze data without the cloud?

Advances in big data and cloud computing have developed these technologies to complement each other. While data analytics interprets huge data into meaningful insights, cloud computing makes this process light and convenient. But can they work without each other? Let’s consider how your data analytics platform would work without cloud computing. Your business will need to purchase computing infrastructure to handle the huge amount of data processing. You will need a system with high processing speed and huge storage capacity. This system will need access to servers, bandwidth, power, and cooling. It will be similar to the time you installed heavy gaming software by clearing out your favorite vacation photos.

Once you have found the system that can handle this you will need to purchase a robust analytics application. You will need to install, configure, and run the application in this system and feed it with all the data of your business. It will process this data and give you meaningful insights.

Need a change? The application will need to be modified and deployed to reflect the change. You will probably rethink every change you need. Even with all this, sharing insights becomes complicated. Let’s not forget the dependency on the system in use. These traditional systems require huge investments and always fear the test of time. With technology changing every fortnight it takes a huge commitment to be using outdated systems simply to justify the money and vision invested in them.

Data analytics in the cloud will reduce your dependency on the desktop. The big data cloud architecture will handle all the processing of the data at a remote server. By using aggregators and integrators, the client’s data sources are integrated into data warehouses that use SQL, Hadoop, Hibase, MongoDB, DynamoDB, and other such data management frameworks. This is then passed through a cloud-based analytical engine that interprets this data followed by a reporting engine that uses data visualization to bring you what you need. This will clear up the processing space of your system. 

You will have remote access to the entire data computing library that is integrated easily with your existing system. What happens when you request a change? It is simply built and integrated directly to the cloud, reducing your deployment time while you continue to access your daily reports uninterrupted. Sharing these insights becomes convenient with SaaS Dashboards. You simply sign in through your preferred browser, view the reports, export them into the format you prefer, and share them. Everyone who has access to the data cloud is connected and on the same page. It bridges the dependency of your local desktop environment by transporting everything to the cloud.

Check out our exclusive interview with Google tech sales specialist and a startup mentor, Mr. K C Ayyagari to gain insights on cloud technology and its role in the analytical transformation.

What is big data’s relationship to the cloud?

Big data management forecast- Courtesy Forrester

Forrester Research survey in 2017 said that big data solutions via cloud subscriptions will increase about 7.5 times faster than hosted desktop options. Let us consider a cloud-based analytics system. According to comparethecloud.net, “The SaaS Dashboard concept is a way in which to present a cloud-hosted application suite to a user without the need to use a hosted desktop interface.”

Big data impacts and benefits using cloud computing

  1. Just a click away

Having everything at your fingertips means that you don’t need to depend on anyone. Besides having all your data insights readily at your fingertips, cloud services are on demand. You can choose how long you want the service and pay only what you use.

  1. Easily shareable

Do you need your sales team to know that they need to up their efforts to meet targets? Simply export the dashboard reports or let your managers view the SaaS dashboard.

  1. Integrated

You can easily have all your applications work easily with each other through cloud-based analytics.

  1. Never lose your data

All your data is backed up all the time on remote servers and data warehouses. You save a lot of storage space by simply putting everything on the cloud.

  1. Fast and lightweight

Do you want to change the way your data looks? Need your data insights designed specifically for your system? Cloud-based analytics reduces deployment time required to generate reports and helps you control your data anytime.

Big data processing in cloud computing environments helps you rent access to software without creating a hole in your pocket. At geniSIGHTS, we understand big data implementation costs and how resource sharing still needs a personalized design for your business needs. With FLASH, our AI-powered dashboard that gives you data analytics in cloud environments (or on-premises), you will never have to worry about anything but your business. Using the cloud has made FLASH highly customizable to fit your business needs. You can simply integrate it into your existing systems. It’s AI-based data insights that use machine learning to adapt based on your decisions. Learning from you as you learn from it. Conversational AI-powered voice control reports that are lightweight and easily shareable through the cloud.

Check out our AI-driven intelligent solution, FLASH, on our website: https://flash.genisights.com/

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Risk and calamity management through AI

When we think of risk management data analytics, the first thing that comes to our mind is financial business analytics. The risk involved in financial transactions is well known. Analyzing data eases the uncertainty in stock market investments. It is also widely used in banking systems to predict loan defaulters and predict fraud.

Read our article on: How AI can help revive industrial operations from COVID losses?

Why should you invest in Risk Management through AI?

1.       Saves bigger future losses

Art or risk management goes beyond financial data science. The world of business is more than relying on investments in insurances to tackle risk. Today, it is important for every business to foresee potential risks and work on solutions to remove or normalize the effect before the crisis hits. Investing in data reporting dashboards that use predictive analysis helps your business save much future damage. Reporting dashboards make repetitive work like monitoring and weekly reporting automated. This not only saves precious man-hours but reduces the risk involved in humans handling repetitive tasks. A global bank used technology to detect unacceptable false-positive rates in anti-money laundering (AML) detection—which were as high as 96 percent. Using machine learning and artificial intelligence they were able to identify crucial data flaws. The data-quality improvements made saved an estimated 35,000 investigative hours. Another example is the use of advanced analytics by Des Moines Public Schools that identifies and helps at-risk students. Using predictive analytics they are able to give students the attention they need so that they don’t have to drop out of school. They help analyze risks in real-time and provide insights to improve your business.

2.       Market analysis and customer response monitoring

Analyzing the response of customers to existing products can help you predict responses for future launches. Breaking down customer data and customer behavior trends by looking at your customers’ transactions to better understand and predict the attitude and behavior of your customers is called Market analysis. Let’s consider the reason for the failure of the famous case of Pepsi Blue. The lack of market research before launching the product in India failed to point out how people felt the color resembles that of Kerosene. Pepsi’s decision to sell the product under the name “Pepsi” confused the customers who were not expecting a berry flavor to their preferred soft drink. The failure of the product clearly points to the importance of analyzing your market. Artificial intelligence can help you understand your customers and their needs. Continuous monitoring of customers’ behavior trends and by adapting to their ever-changing moods, AI can point you to the preferred audience for your products. It can give you insights like the right time to release a product, spending habits of your customers, and their response to the social, economical and political issues of the world by analyzing their social media presence. This will help you make better decisions and reduce the risk of making these decisions.

3.       Helps you manage a disaster

Real-time Artificial intelligence monitoring helps you predict the possibility of a disaster. But in these uncertain times, we all know that disaster can come from any direction in any form. How then can you help your business restore its health?

Today’s complex algorithms help you come up with solutions by looking at the bigger picture. They break down your data into tiny units and analyze the possible ways to revive. Let’s consider how many chess combinations can you come within a second? IBM’s Blue Gene/L can routinely handle 280 trillion operations every second. A single scientist with a calculator would have to work nonstop for 177,000 years to perform the operations that Blue Gene could do in one second. Today’s complex algorithms can help you analyze all your options before deciding the one right for your business.

How can AI help in calamity management?

HCL recently partnered with the Tamil Nadu government to set up a Disaster Management-Data Analytics Centre. Data analytics using complex AI algorithms can help governments capture data trends in real-time and help the government modify decisions on the go by analyzing the response of the people to these decisions. Use of AI-powered drones to monitor and report vast areas in disaster-affected areas was used by the government of Uttarakhand and Kerala. Analyzing this huge amount of data allows response teams to mark affected regions and conduct risk and damage assessment promptly from remote locations. By predicting the displacement of people using AI rescue teams can meet citizens midway and even direct them to nearby shelter locations. With the world slowly trying to revive the economy, it is important for the governments around the world to keep a close watch on the data generated and use meaningful insights to improve the failing economy. Governments can analyze emergency calls and improve turnaround time in solving these emergencies. They can also monitor the availability of essential services and suggest ways to improve the supply chain management of these sectors.

  1. Identify vulnerabilities and hotspots

The use of processes like the analytic hierarchy process (AHP), helps to identify the factors that can cause failure and analyze the effect of these factors by determining the probability of these factors. A study conducted on mapping disaster vulnerability in India using Analytical Hierarchy process helps mark the level of disaster vulnerability across the country and identify hotspots

Even in our business, we need to identify the areas where there is a high possibility of failure. AI can help in identifying these hotspots and make us aware of our business

  1. Examine interdependencies

Interdependencies turn disasters into a full-blown domino effect. Being aware of interdependencies can help make better decisions. In times of calamity, you need to take into account the shortage of essential services and the grocery supplies as well. AI algorithms recognize these interdependencies and see help connect the dots to form a big picture. Real-time monitoring helps keep a check on all possible pain points and reduces the effect of a disaster by giving quick insights to stop the chain reaction of a disaster. Even in business, it is important to understand the dependencies of each node and ensure a plan in case the tiles start falling.

  1. Connect the links

In case of a disaster, it becomes important to achieve clear communication. You have recognized the problem, found a solution, but does everyone know what to do? Human communication depends on perception and can cloud the message. AI can help come up with a clear roadmap and distribute responsibilities. Having a common dashboard for communication reduces assumptions and helps keep everyone clear about the plan of action. Reporting through drones and sensors, broadcasting plans of action, and forecasting where people will go through predictive analysis help the process of disaster management to be more controlled.

  1. Understanding the cause and effect relationship

We have already established that AI can try multiple combinations at a time to come up with an optimal solution. But it is important to monitor the effectiveness of the decision taken by monitoring the response. Social media monitoring can be a powerful tool to understand the effect of a disaster. It can be used for communicating with the public and for sentiment analysis to understand how people are reacting to a situation.

geniSIGHTS has developed an AI-driven platform for unstructured data. Its complex algorithms analyze social media trends and predict sentiments on business & business parameters – Net Sentiment Score, Sentiment trends, etc. Rightly named as “Ordo Ab Chao” which means Order from Chaos. It structures and filters social media to derive meaningful insights into how a disaster is accepted by the general public. The damage done to the image of companies in the face of a disaster can be viewed and monitored. Efforts to revive the trust can be analyzed to plan better disaster management.

Check out Ordo Ab Chao to see how people are reacting to the COVID-19 pandemic at: https://flash.genisights.com/ordoabchao/

Also check out our Tamil Nadu COVID19 Information Tracker bot for quick updates on Tamil Nadu cases: https://genisights.com/CovidBot/

The disaster was once considered unpredictable and uncertain. Today Artificial Intelligence Risk Management is helping the world change this definition. Predictive analytics risk management helps us be prepared and anticipate calamity before it strikes. Prevention is better than cure. Continuous monitoring, highlighting hotspots, and noting the interdependencies can help achieve this. Even with all the preparation when disaster strikes there is much to do to respond to the hit. AI helps come up with effective ways to soften the impact. The next step is to revive from the attack with optimism. Complex algorithms to restore the economy and the people after the calamity strikes are an important part of risk management. The importance of seeing the whole picture and multi-tasking responsibilities can be easily handled by AI. It is important to handle tough times without letting emotions take over. Relying on machines ensures that decisions are unbiased and bold enough to be outcome-oriented.

Why does your business need a real time business analytics dashboard?

 What is a real-time dashboard?

According to research conducted by DOMO, we create an estimated 1.7MB of data every second for every person on earth! But data is only useful if you can derive meaning from it. A real-time dashboard simplifies this task for you. It is a pictorial representation of data that is automatically updated with current information to derive meaningful insights.

It is an at-a-glance view of what is going right with your business and what can be the improvement areas. It focuses on your KPI (Key performance indicators) and helps you in better decision making.

What kind of information can a dashboard provide?

The main purpose of a dashboard is to tell you a story, the health of a business, or a specific process based on the Key Performance Indicators (KPI) of your business. Dashboards are versatile and can be used for different markets like Retail, E-tail, Travel, Telecom, Healthcare, Sales, and Marketing. Real-time dashboards help you to break down data like your past transaction data and point of sale data to better understand your customer base.

You can have KPIs like customer retention rate, attrition rate, Net promoter rate, and market growth rate. If you are a call center business you will need the response time of your employees, customer satisfaction, resolution rate, and live tracking of your ticket volume trends. Having a real-time sales dashboard can help you keep track of your sales by monitoring high sales regions, customer acquisition costs, and even forecast future sales. It can also keep track of your social reach and website traffic by monitoring your online presence.

The use of a dashboard will also vary from employee to employee. If you are a project management head you can keep a check of the cost of work done and estimate completion of projects. But as a CEO you will focus on ROI, Operating margins, and predicting the company revenues.

Dashboards are versatile and can simplify any information to benefit your business model.

6 Reasons your business needs a real-time dashboard

1.   Time is money

A  company can save over 40 hours per week by shifting to a dashboard reporting software. The time saved in getting the information translated into meaningful insights can be utilized to work on actions. Reports are usually generated at the end of the month because they are time-consuming. By using a real-time dashboard the only time consumed will be in setting up the dashboard, which is minimal to the overall time saved. With the rise in Conversational AI-based dashboards, the value for time and convenience has increased

 2.   All your data at your fingertips

Data never sleeps. Why should your dashboard? With the amount of data generated daily maintaining it becomes tedious to maintain data. Real-time dashboards ensure that all your data is accounted for. You can monitor KPIs and metrics from different platforms under one location. Your data is clean and the focus shifts to outcome-based decisions that can be monitored on the fly.

3.   Accessible to all

Gone are the times when the understanding of business was confined to the sharing of details at business meetings. According to research by BARC, the use of cloud planning has jumped from just 8% of survey respondents in 2016 to 36% in 2020. Dashboards use cloud computing to ensure that the data is accessible and transferable to everyone through your desktop or mobile. Dashboards are simple to access and can be understood easily by any employee. They improve internal communications and transparency in the business. They ensure that every member of the team is on the same page.

 4.   Reduced risk

With real-time reporting failures are highlighted at their initial stages. This reduces the time of diagnosis and helps divert attention to solutions rather than damage control. With an increase in uncertainty in business, it is important to be on your toes at all times and ensure your business grows.

5.   Leveraging artificial Intelligence

Dashboards need to adapt to your business requirements and prioritize your business targets. For readily available insights, dashboards do the task of report development, management,  testing/validation, and deployment in no time. Using advanced machine learning and artificial intelligence algorithms, smart dashboards help support dynamic business queries.

According to Gartner “Continuous intelligence is a design pattern in which real-time analytics are integrated within a business operation, processing current and historical data to prescribe actions in response to events. Continuous intelligence leverages multiple technologies such as augmented analytics, event stream processing, optimization, business rule management, and ML.”  You can use augmented BI based dashboards for decision automation and for providing decision support to improve your business.

 6.   Customizable

No one size fits all. Dashboards understand the need to design visual representation based on your needs. They understand the highly volatile nature of business and adapt to your requirements. They are highly customizable and user-centric.

Dashboards can help simplify the tedious task of reporting and help you focus on growing your business. Real-time monitoring can help your business to stay ahead in the race and transform your weaknesses into your strengths. 

Realizing the promise of an AI-driven world, geniSIGHTS has created ‘FLASH’. ‘FLASH’ is a first-of-its-kind artificial intelligence-powered dashboard with voice support to enable businesses in processing big data in real-time. It is highly customizable and scalable according to your business needs and can be easily integrated with existing systems. Dashboards need to cater to your changing business needs by continuous machine learning and by adding conversational AI to advanced systems FLASH has made reporting easy and convenient for everyone.

Rajesh Kumar, the Founder of geniSIGHTS who strongly believes in AI-driven technology says “ Today’s business insights provide good reports but do not go beyond the basics. Flash is an AI-driven product that empowers the business users to quickly experience advanced analytical insights thereby significantly bringing down the development time.

Check out the FLASH dashboard on https://flash.genisights.com/

Also check out Ordo Ab Chao, A sentiment analysis dashboard that uses Social media on https://genisights.com/ordoabchao/#!/home

Dashboards like FLASH are transforming advanced real-time reporting to a whole new level. According to a report by Deloitte “Early benefits from the adoption of Conversational AI mean the global AI-derived business value is expected to grow by an average of 30% annually”. Businesses are undergoing a digital transformation and the advancements in Self-Service BI have made employees and key decision-makers independent and confident even with a lack of knowledge in data management. The rise in Conversational AI-based dashboards has made reporting simpler and faster than ever before. It is time for you to shift your business to a smarter, cleaner, and faster data-driven real-time business analytics dashboard.

Transforming Banking with Artificial Intelligence

The use of non-branch banking solutions has increased dramatically and especially in COVID times.  Before COVID-19, digital transformation was but a plan on paper, with only 15% of the major banks digitally transforming while for most banks and financial institutions things changed after the pandemic.

According to a recent survey by J.D. Power, only 46% of consumers will go back to “banking as usual.” The biggest change will be in the increased use of mobile banking (20%), increased use of online banking (17%), and decreased uses of branches (10%).

35% of consumers stated that they had increased the use of online banking (laptop or PC) since the COVID-19 crisis, with 17% stating they have used this capability much more.

Banks, credit unions, and financial institutions must use this time of disruption to consider reinventing themselves during this time of the pandemic. This is the right time to use data, AI, and technology to impact innovation and the digital delivery of services and solutions. Improved use of data and advanced analytics can provide meaningful insights that can improve customer engagement and experiences, streamline financial operations, and be a driver for digital transformation.

AI has remodeled banking vastly and can still do provided we tend to harness it properly. AI is empowering Banking and Financial institutions in fraud detection, credit risk scoring, Anti-money laundering (AML), and churn prediction.  BMO is the first Canadian bank where customers use mobile devices to open bank accounts. The Royal Bank of Canada provides a dynamic experience to employees from their first day on the job.

Customer support and the front workplace of banks are already chatbot enabled. Further, with the support of supported past interactions, AI develops a much better understanding of consumers and their behavior. Every client would favor a special methodology for treating their finances. With  AI, banks will customize monetary products and services by adding customized options and intuitive interactions to deliver meaningful client engagement and build sturdy relationships with their customers.

With its power to predict future eventualities by analyzing past behaviors, AI helps banks predict future outcomes and trends. This helps banks to spot anti-money wash patterns and create client recommendations. cash launderers, through a series of actions, portray that the supply of their hot cash is legal. With its power of Machine Learning, AI identifies these hidden actions and helps save millions for banks.  AI is additionally in a position to find suspicious information patterns among whopping volumes of information to hold out fraud management. With increasing online banking facilities this can be of utmost required.  Further, with its key recommendation engines, AI studies the past to predict the future behavior of information points that help banks to successfully up-sell and cross-sell their products.

About 32% of financial service providers are already using AI technologies like Predictive Analytics, Voice Recognition, among others. (Source: Wipro).  AI is integral to the bank’s processes and operations and keeps evolving and innovating with time.  AI can alter banks to leverage human and machine capabilities optimally to drive operational and price efficiencies, and deliver customized services. AI won’t solely empower banks by automating its data personnel, it’ll conjointly create the full method of automation intelligent enough to try to do away with cyber risks, stay in the competition, predict patterns and provide recommendations. Amazon has generated 35 percent of its revenue from its recommendation engine.3 Netflix saved $1 billion in customer retention using its recommendation engine.3 C

Conversational AI (CAI) systems can be rapidly deployed to give a service boost and better decision making during and after challenging times. Many banks have adopted CAI based processes with the use of chatbots. HSBC introduced the digital financial assistant Amy while Bank of America’s AI-powered digital assistant, Erica, has more than ten million users and completed 100 million client requests in the first 18 months since its introduction.  Banks can engage customers with two-way communication with the use of a chatbot or an interactive assistant powered by AI to intelligently respond to their queries other than routine questions to gain a competitive edge.  By adopting AI, leaders within the banking sector are already taking actions with due diligence to reap these advantages.

In e-Conversation with Mr. K C Ayyagari, Google!

On the occasion of geniSIGHTS stepping into the 5th year of operations, our HR Head, Mr.Sridharan J.S hosts an e-conversation session with Mr.KC Ayyagari, Engineer @ Google, Experienced Tech Sales Specialist and a Startup Mentor on ‘Steering ahead in the times of crisis’, how cloud infrastructure is at the forefront now for delivering customer satisfaction.

Hi Mr. KC; We are happy to host an e-conversation with you during these trying times.

Sridharan – The cloud ecosystem is a broad one, but there are some common trends that have emerged. Tell us some insights into how cloud infrastructure has changed in the last 3 years.

KC – There are many, but in my view, here are the 3 main trends I can think of:

1) Cloud-native – GenNext companies, startups, and SaaS companies, etc., are pioneering this.

2) Hybrid or Multi-cloud – This trend is picking because of the various factors but mainly because companies don’t want to miss out on innovation from any cloud provider. IT/ITES companies, Consulting companies, service industry companies, etc., are pioneering this.

3) Cloud Burst – Here is where companies are looking to use the cloud for expanding their on-premise infrastructure when needed. Legacy companies who already invested in their data centers, manufacturing, and retail verticals companies, etc., are pioneering this trend.

Sridharan – How is the sudden surge in cloud technology seen during the pandemic?

KC – Cloud computing is keeping us all connected to this pandemic. Many health care organizations started doing remote consulting, all the IT workforce started to work from home, individuals are using video calling services, retail organizations moved their entire orders to online, etc. All these are unplanned workloads for any organization and most of them have to depend on public clouds for the same. This brings to one of the main trends I mentioned above, i.e., Cloud burst. This is also the time where cloud providers also started re-evaluating their products and started releasing products to facilitate these changes all over the world. For instance, Google Cloud released products like BeyondCorp Remote Access; a service lets your employees and extended workforce access internal web apps from virtually any device, anywhere, without a traditional remote-access VPN.

So to say there is a surge in cloud usage and at the same time an increase in the number of different useful cloud services released by Cloud providers.

Sridharan – We know Cloud technology is the backbone for IoT and in order to evaluate data profitably, Analytics companies like ours are aware the hybrid cloud is increasingly becoming more important and Big data produced by the IoT devices are going to be provided by the cloud. What do you think will the demands be like in the upcoming 1 to 3 years?

KC- In general, as per McKinsey & Company, it is estimated that hybrid, multi-cloud is set to be a USD 1.2 trillion market opportunity by 2022. Usage of Cloud agnostic solutions like Kubernetes is on the rise. If you ask me why this is happening, there can be many factors like companies that want to use the best hardware available to the best of cloud services they can use.

Sridharan – Do you think a success factor for analyzing big data could be analytics itself that could provide a seamless link to the data hosted on the cloud?

KC- Today the Vs (Volume, velocity, and variety) of data even within a small startup is in terabytes. Data is as good as the time it is analytics. So yes, the success factor for analyzing big data could be analytics and a seamless link to cloud hosting makes this entire process faster irrespective of the size of Vs organizations are dealing with.

Sridharan – What are the do’s and dont’s for companies not in cloud wanting to move to the cloud, now, during the pandemic?

KC – These are some, I can think of:

• Act fast

• Be prepared for a Cloud burst if your apps are running on your private cloud.

• Start with or migrate to cloud-agnostic technologies like Kubernetes to build your apps.

• Use serverless services like Cloud Functions (in Google Cloud) where ever possible.

• Strengthen your DevOps practices. Make sure CI/CD is set up.

• Be prepared for anything and document everything in the company. Even though it’s hard to say this, it’s not good advice to just depend on your star employee for everything. Be prepared to give him time off or worst case sick leave.

• Listen to the customer. This is the time you need to understand customer trends more than ever.

• Plan your budget properly. Don’t move services to the cloud if you cannot pay for them. Move only the much-needed part of your product.

• Don’t just keep on releasing new features. Now it’s not the time. That comes later.

Sridharan – Are there any additional data security features introduced for the upcoming demand for the cloud?

Kc – Nothing I can think of, especially for cloud demand. Make sure your services are well protected, always irrespective of demand.

Sridharan- What are the new add-ons or features that we can see in cloud technology in the next 3 years?

KC – This is my view: I think we will see more services that support companies with remote connectivity, hybrid cloud, and multi-cloud.

Sridharan – Cloud computing has been a Top 3 trend in IT since it took root with the introduction of AWS’s S3 data storage in late 2006. Where is the cloud technology heading?

KC – In my view Cloud computing would be the defacto standard for running most of the workloads in any organization; big or small. Within the cloud, in my view, serverless computing and microservices are going to be the future.

Thank you for taking the time to have a conversation with us, it has given us a lot of insights.

Thanks again and stay safe!

How AI can help revive industrial operations from COVID losses?

COVID outbreak has affected many industries with some sectors being affected badly. The infection is hitting associations hard from everywhere throughout the world. Businesses across the globe are adapting to the ‘new normal’ of working remotely.

In this time of crisis, Artificial Intelligence can be a great aid for businesses to boost their operations and increase productivity. This article will discuss how AI can help revive industrial operations from COVID impact.

  1. Demand & supply: Organizations always tend to maintain the balance between demand and supply to avoid excess inventory. In this time of crisis, maintaining this balance is critical to achieving as, even if the demand data is available in the open sources, production is either at a halt or is operating with minimum human support. For industries like manufacturing, steel, factory, etc production, their basic activity, operates with human resources. But organizations who have upgraded themselves to industry 4.0 have AI in place to help and enhance human taskforce. AI can perform the same tasks as that of a man but at a faster pace. AI needs data to learn and during this COVID time availability of current data might be a challenge for the organizations. Organizations are using data to train AI to be representative for it to learn the patterns and intents, for demand forecasting and optimal supply chain distribution.

  2. Administrative jobs: Artificial intelligence can perform admin tasks like scheduling meetings, tech support, issuing refunds, order tracking, etc with accuracy. This way AI supports human resources by enabling them to focus on value-added activities. AI automates the tasks using machine learning. Advanced AI can perform structured and unstructured tasks from its learning algorithm. This capability will allow organizations with a fair amount of work in a short span of time.

  3. Revenue forecasting: Generating revenue in the next few months will be a big challenge for organizations especially for small and medium businesses whose operations are at halt currently because of lockdowns in several parts of the world. Cash flow becomes critical during times like these pushing accounts to maintain data on the path of cash-flows. This data when feeding to AI, will analyze the purpose of cash-flow and forecast the paths where the cash should flow to gain revenue. The data fed or provided must be accurate for AI to predict actionable insights that would benefit the decision-makers. Many organizations are already moving towards revenue loss but with AI they can streamline the cash-flow with profits.

  4. Staffing and infrastructure planning: Because of this pandemic and the worldwide lockdown to maintain social distancing employees are staying indoors. Work from home is followed by several organizations but this method is not applicable to all industries. Industries like manufacturing, automobiles require their staff to be physically present mostly in the production and technical field. AI can benefit these industries to identify the number of staff required to be present in the field so that the operations can continue with the minimum workforce. Following revenue losses, many organizations are laying off staff to balance their revenue profits. AI can be utilized to decide if the number of layoffs is correct, more-skilled employees should retain, and many other decisions.

The artificial intelligence network is working profoundly to deliver its applications in every sector to fight the COVID pandemic. But the AI framework is still at the nascent stage and will take time and data to train themselves to fight the next pandemic with substantially more effectiveness and efficiency.