Financial Services industry has been at the forefront of using innovative technologies. During 2000s, banks and insurers rolled out ‘anytime anywhere’ transaction capabilities – thanks to centralized Core Banking Systems and e-channel solutions. In the new wave of digitization, the players are now leveraging technologies like Intelligent Automation along with Artificial Intelligence and GIS.
Even after computerization, transactions like account opening, loan application processing, NEFT / RTGS recon, payment gateway recon, regulatory filing and customer servicing were people intensive. The banks are now rapidly deploying RPA, OCR and AI technologies to make these processes truly automatic. Intelligent automation is able to read account opening and loan applications, validate fields and enter them in Core Banking System. Intelligent OCRs are able to download financial statements of loan applicants, analyze them and present smart summary to loan assessing officers. RPAs are able to handle customer queries using Natural Language Processing techniques and AI is helping banks in fraud detection, customer servicing and investment advisory.
Insurers too are leveraging RPA and AI for policy issuance, risk based pricing, claims fraud detection, regulatory reporting, customer servicing and improving renewal rates. GIS tools are helping insurers in improving risk-based underwriting, disaster support and reinsurance strategy.

Featured Solutions

+ - Improving customer care using intelligent automation
Companies tend to operate customer care departments 24*7 which is operationally difficult. Intelligent Virtual Assistant can be used to replace the customer care departments which are powered by Conversational AI. They offer several benefits such as round the clock support, zero wait time, consistent in response with zero compliance issues and smart routing to live agents if required. In financial services domain, conversational AI can help in resolving standard issues in a few minutes increasing customer satisfaction. Chatbots in conjunction with advanced Natural Language processing can give human like services to the end users. These Bots can further retain the learning from human interactions and can learn substantially over time. Logs of these conversations once analyzed over time can help in identifying interesting facts about customer behavior using sentiment analysis.
+ - Customer segmentation using Machine Learning
Customer segmentation analysis is one of the established techniques of sales and marketing function to serve existing customers better, improve profitability and enhance ability to target new customers. But customer segmentation methods have evolved lately in terms of how they are carried out. Traditional techniques involve segmenting customers based on age, education, location, income levels, etc. However, recently with more data and analytical tools available, strategists are often letting the machine learning tools to explore the parameters which bind various customers together. And these parameters could be in terms of customer behavior (e.g. informed customers who like seek detailed specifications vs those who buy for discounts), extent of support the customer needs (higher support would cost company higher) or frequency and ticket size of purchases. With unsupervised learning techniques of machine learning, it is possible to get the algorithm explore customers segments which managements may not be explicitly aware of. Such segmentation can help companies determine appropriate product pricing, offer product bundling, develop customized marketing and suggest specific financial products to the consumer. We can help you with conducting ML-based customer segmentation to improve your understanding of customers so you can better serve them and articulate a smarter customer growth strategy.
+ - Improved regulatory reporting using RPA
Be it insurers, banks or broking industry, financial services industry is engulfed with heavy regulatory reporting requirements involving daily, weekly, fortnightly, quarterly and annual reporting. And since most of the financial services players have numerous systems, collating data and generating reports is often very tedious, time taking, error-prone and resource heave exercise. Missing regulatory reporting could result in fines from the regulator and hence, many players are forced to deploy dedicated teams to prepare reports and submit as per the guidelines.
Companies can save the effort and improve compliance to regulatory reporting by using RPA tools. To begin with, RPA tools can assist in collating data from many structured sources (e.g. IT systems, Excel sheets), semi-structured sources (e.g. PDF, images) as well as online sources (e.g. websites). Once these are converted into digital and structured data, RPA can help carry out data computations to arrive at ratios, benchmarks or percentages as required by the regulator. RPA can further arrange the data in pre-defined formats with covering notes and even email out to the regulator after due checks by the regulatory team of the company. This can help you save costs, never miss submission deadlines and reduce errors when complying with regulatory reporting requirements.
We can help you map the data sources and deploy RPA tools for regulatory reporting by leveraging our knowledge of financial services regulations and skills of deploying RPA tools.
+ - Insurance Risk Management using GIS
Geographic location is an important dimension for insurance business. Be it addresses of customers, risk based underwriting, insured asset tracking or claims handling, location plays an important role in insurance processes. Mapping customer locations along with areas which are more prone to insured risks like natural calamities can help them in conducting risk-based underwriting and premium pricing. Using GIS, the underwriters can monitor how much business has been booked in risk prone areas on a map using color schemes. In case of motor insurance, insurers can use location-based data such as traffic patterns, accident hot-spots and road quality to carry out informed risk assessment of vehicles. Once underwriting is done, GIS can be used to track the assets which are insured using IoT or GPS based systems. In case of disaster, using GIS insurers can offer prompt claims services to customers in the affected areas. Using GIS, insurers can schedule their surveyors based on their locations and route them optimally. GIS can help estimate the number of cases in a geography and hence assess insurance reserves more accurately.
Lastly, insurer can keep updating GIS maps with history of claims and hence build its own intelligence of risk-prone geographies by the type of insurance.
+ - Customer analysis using GIS
One of the challenges encountered nowadays by sales and marketing specialists is customer profiling analysis. Knowing where your clients live and what their characteristics becomes a necessity. By taking these aspects into consideration, existing products can be made available in the right geographies and products can be easily tweaked in order to meet the target customers’ desires and needs. Geographical information systems can make a huge difference in carrying out customer analysis and profiling with a geographical dimension using GIS solutions.

GIS is a technological tool for comprehending geography and making intelligent decisions. It gives any organization the ability to go beyond standard data analysis with tools to integrate, visualize and analyze the data using geography. Market potential assessment, customer analytics and site selection are ways businesses can combine geographical analysis for better business intelligence.

GIS can help in answering several questions such as:
  • Location – What market opportunities exist at a particular location (district, locality, PIN code)?
  • Trends – What are the buying and behavior partners at different geographies?
  • Models – What spatial patterns exist?
  • Modeling – What would happen if?

Customer analysis can be done using GIS which adds Postal address or longitude and latitude stamps to business data and visualizing the data on a map. For example, while setting up a children clothing store, we could map the population of people with children in targeted age group throughout the target geographical area like states or districts. The data once put into a GIS can generate maps wherein the highest concentration of families with children are depicted using specific color patterns. The final map so generated will highlight the ideal areas for opening new stores. Similarly, banks can visualize customer data on a map with different color schemes based on parameters like credit worthiness, income levels, no. of bank accounts customers have, average bank balances and expenditure patterns. This helps identify cross-sell opportunities, opening of ATM or Branches and run intelligent campaigns.

GIS allows businesses to convert bytes of data in legacy system databases and Excel sheets to be presented in a more visual and understandable form, thus enabling business managers to get better insights and take more informed decisions.

Case Studies

A large Mutual Fund in India

The leading Mutual fund in India receives numerous emails every day from customers. Handling these emails required huge manual effort even though most of these queries were repetitive in nature. We helped the Mutual Fund deploy intelligent automation involving RPA and NLP to interpret incoming emails and respond to them

Read More

A large broking company

A leading Broking company offering online trading platform wished to identify newly registered customers who had higher propensity to start transacting. We helped the company develop Machine Learning based model to predict propensity to transact for members who downloaded their mobile app

Read More