Technology at the heart of the modern banking experience


The Indian banking and finance sector is on the cusp of transformation. The pandemic has acted as a catalyst for the demand for advanced digital services and technologies powered by AI and ML. And as these technologies mature, artificial intelligence and machine learning become smarter and more relevant to the processes of financial institutions.

The number of areas where AI and ML are being leveraged around the world in banking and finance is constantly growing. Use cases in different areas of banking and finance, such as automated customer support, real-time fraud detection, better customer data management, risk modeling and marketing strategy planning, are growing exponentially, and every financial institution can take advantage of this to improve their processes. M. Gautam Samanta, Executive Vice President and Global Head of BFS, Coforge in a discussion with CXOToday share more information about the same

What constitutes the modern banking experience?

The financial ecosystem is in uncharted territory and the situation is changing every day. Today, it is imperative for financial services organizations to become flexible, agile, transparent and act decisively in response to the challenges posed by various entities. Connectivity and openness are at the heart of modern financial services. They enable banks to deliver superior banking experiences, thereby promoting customer acquisition and customer retention.

For an evolving banking industry, here are some modern solutions that can help make financial services faster and more profitable:

  • With customer relationship management (CRM) technology, organizations can collect and analyze data and create detailed customer profiles, which internal advisors can use to get a 360-degree view of the customer and their unique situation. This level of knowledge is extremely valuable as it enables advisors to offer personalized advice to clients at every stage of their financial journey.
  • AI-enabled chatbots extract and process information from various sources such as the bank’s knowledge base and CRM customer profiles, to respond to incoming customer service requests. If a particular request is beyond the capabilities of the chatbot, it is automatically escalated to a live service representative who can help the customer find a solution.
  • Automating improves efficiency and eliminates many of the bottlenecks and paper-based processes that plague traditional banking. This faster, seamless experience can help improve the customer experience and speed up processes with less heavy lifting on the part of bank employees, meaning more customers can be processed in less time. time.

Why banks need to adopt AI and ML and what are their benefits?

Apart from data security, data insights are the main differentiator in today’s banking scenario. AI and ML together provide the ability to analyze this data and gain deep insight into customer and market behavior. Financial institutions can use the data to revise their strategies, improve the customer experience and prevent fraud. AI and ML in banking can automate many processes, leading to increased productivity and cost savings:

Benefits of implementing AI&ML in banks:

  • Fraud detection– All banking and financial institutions are adopting AI and ML to anticipate and reduce fraud. With the rise of digital banking, multiple transitions are happening at the same time across various mediums and AI and ML technologies have made it possible to track and alert in case of irregularities or errors. These technologies can analyze vast customer data, patterns and behaviors and help future-proof banking processes.
  • Customer service – Customer service is at the heart of banking, conversational AI and ML are now changing the customer experience with chatbots and real-time feedback. Virtual assistance like Alexa, Siri, etc. uses upselling and cross-selling to retain customers and maximize bank revenue.
  • Credit service and loan decision – By leveraging AI and ML, banks gain a better understanding of credit and market risk so they can reduce loan underwriting risk. Credit and lending decisions are now made by automated underwriting engines that process millions of data points and historical data to determine creditworthiness.
  • Regulatory conformity –Automated transaction monitoring is a key application of machine learning in banking. ML-powered predictive analytics platforms have already made an impact and helped monitor AML transactions and reduce false positives. The KYC (Know-Your-Customer) process also benefits from AI and ML with the use of facial biometrics and ML-based scoring to ensure better compliance.

Using AI and ML in Mortgage Processing and Asset Management

The COVID-19 pandemic has created a boom in the new home purchase loan and home refinance markets. This has led the transformation of mortgage processing to become a very important aspect of financial digitalization. Many financial institutes are now working to create a multi-pronged strategy to eliminate obsolescence while engaging in holistic digital transformation.

At Coforge, we provide many solutions to banks and mortgage lenders for asset processing and management, helping them increase productivity and save time. Some of them include:

  • Intelligent Data Processing (IDP): This solution helps mortgage lenders and banks digitize various documents and enables real-time processing using AI and ML technologies. It also integrates statements and credit reports through open APIs.
  • Coforge also has another proprietary platform called Loan Accel which identifies gaps and searches for updated documents, assists in updating and indexing data, and validates documents to provide real-time status. . It also provides an AI-driven decision system for underwriting.

In one of our case studies, we found that these solutions helped a bank close the mortgage process in 16 days vs. 31 days and generated a 77% increase in one-touch submissions to subscription.

How are companies leveraging AI and ML for risk management?

Risk management has always been a central point in the world of finance. As the digital economy grows stronger and new technological innovations constantly emerge, the risks associated with private and commercial data are also catching up. However, AI and ML-based technologies can help detect and mitigate risks in today’s data-intensive environment.

At Coforge, we combine our domain knowledge with expertise in Big Data, EDA, API and Low Code platforms to deliver differentiated value. Using innovative technologies, we can forecast, predict and identify fraud in new business and claims, especially in personal lines, by leveraging large volumes of data.

  • In case of loan or credit risk – Lending is a critical revenue mechanism for a financial institution, but it also represents one of the greatest areas of risk. Using AI and ML, institutions can effectively measure and manage credit risk. The goal is to minimize potential risks and maximize returns for the business. The value of any collateral pledged by the borrower can be assessed digitally in real time to determine if the loan risk relative to the value of the collateral is in the bank’s best interest. Additionally, these technologies can help monitor credit quality and provide insight into proactive actions that need to be taken if signs of deterioration occur.
  • Unforeseen events and capital shortfall- Financial institutes are leveraging AI and ML to monitor any unexpected risk that needs to be factored in and factored into product pricing. This helps them maintain sufficient reserve capital to manage unforeseen risks that may affect operations and avoid regulatory violations.
  • Operational risks- Manual processes can lead to human error. Fraud or failure of internal governance and control mechanisms are other factors that constitute operational risks. Through the use of AI and ML, financial institutes can automate standardization processes, implement various frameworks, and prepare contingency plans for all sorts of possibilities.
  • Technological risk- As technological advancements have heightened customer expectations, competitive pressures on financial institutions to stay on top are increasing. This sector has traditionally been the first to adopt technological innovations, and it also carries various risks. Downtime, violation of the network perimeter, and technological obsolescence are some of the complexities that these institutes face. This can be countered with robust and proactive enterprise security solutions and a global security operations center to monitor risks powdered by AI and ML. Coforge also provides a security risk assessment solution for a holistic end-to-end view of security posture, vulnerabilities and compliances.

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