Indian banking and financial sector are at the cusp of transformation. The pandemic acted as a catalyst for the demand of cutting-edge digital services and technologies powered by AI and ML. And as these technologies mature, artificial intelligence and machine learning are becoming smarter and more adapted to processes across financial institutions.
The number of areas where AI & ML is being leveraged worldwide in banking and finance is constantly growing. Use cases in different areas of banking and finance like automated customer support, real-time Fraud Detection, better customer data management, risk modelling, and marketing strategy planning are growing exponentially, and every financial institute can leverage them to improve its processes. Mr. Gautam Samanta, EVP & Global Head of BFS, Coforge in a discussion with CXOToday share more insight on the same
What constitutes the modern banking experience?
The financial ecosystem is in uncharted territory and the situation is evolving every day. In the current times, it is imperative that financial service organizations become flexible, agile, transparent, and act decisively in response to the challenges that are been posed by various entities. Connectedness and openness are central to modern financial services. They enable banks to deliver superior banking experiences, ultimately driving customer acquisition and increasing customer loyalty.
For an evolving banking sector, here are a few modern solutions that can help make financial services faster and cost-efficient:
- With customer relationship management (CRM) technology, organisations can collect and analyse data and build detailed customer profiles, which the in-house advisors can use to gain a 360-degree view of the customer and their unique situation. This level of insight is incredibly valuable because it enables advisors to offer personalized advice to customers at every stage of their financial journey.
- AI-enabled chatbots pull 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 exceeds the chatbot’s capabilities, it’s automatically escalated to a live service representative who can help the customer work toward a resolution.
- Automation improves efficiency and eliminates many of the bottlenecks and paper-based processes that are rife in traditional banking. This faster, seamless experience can help improve the customer experience and faster the processes with less need for heavy lifting by bank employees, meaning more customers can be dealt with in less time.
Why do banks need to embrace AI &ML and what are their benefits?
Along with data security, data insights are the key differentiator in the current banking scenario. AI and ML together provide the opportunity to analyse this data and get deep insight into both customer and market behaviour. Financial institutions can leverage data to revise their strategies, improve customer experience, and prevent fraud. AI & ML in banking can automate many processes which lead to an increase in productivity and cost savings:
Benefits of implementing AI&ML in banks:
- Fraud Detection– All banking and financial institutions are embracing AI and ML to anticipate and reduce frauds. With the rise of digital banking, multiple transitions are happening at the same time across various mediums and AI and ML technologies have helped track and alert in case of any irregularities or errors. These technologies can analyse the vast customer data, their pattern and behaviours and help in future-proofing the banking processes.
- Customer Service – Customer servicing is a core part of banking, conversational AI and ML are now changing customer experience with chatbots, and real-time feedback. Virtual assistance like Alexa, Siri, etc utilizes to upsell and cross-sell to retain customers and maximize the revenue of the bank.
- Credit service and loan decision – By leveraging AI and ML, banks get better insight into both credit and market risk to be able to reduce loan underwriting risk. Credit and loan decisions are now being made by automated underwriting engines which churn through millions of data points and historical data to determine creditworthiness.
- Regulatory Compliance –Automated transaction monitoring is a key application of Machine Learning in the banking sector. ML-powered Predictive Analytics platforms have already made an impact and help in monitoring AML transactions and help reduce false positives. The KYC (Know-Your-Customer) process also benefits from AI and ML with the usage of facial biometrics and ML-based scoring to ensure higher compliance.
Use of AI &ML in mortgage processing and asset management
The COVID-19 pandemic has created a boom in the new home purchase loans and home refinancing markets. This has led to mortgage processing transformation becoming a very prominent aspect of financial digitalisation. Many financial institutes are now working towards creating a multi-pronged strategy to remove obsolescence while taking the journey towards a holistic digital transformation.
We at Coforge provide many solutions to banks and mortgage lenders for processing and asset management, which help them increase productivity and save time. Some of them include:
- Intelligent Data Processing (IDP): This solution helps mortgage lenders and banks to digitize various documents and enabled 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 that identifies deficiencies and seeks updated documents, helps with data updating and indexing, and validates documents to provide real-time status. It also provides an AI-led decision System for underwriting.
In one of our case studies, we have discovered that these solutions have helped a bank close the mortgage process in 16 days vs 31 days and generated a 77% increase in one-touch submission to Underwriting.
How are businesses leveraging AI & ML for risk management?
Risk management has always been a focal point in the world of finance. As the digital economy grows stronger and new tech innovations emerge constantly, the risks associated with private and commercial data are also catching up. However, AI & ML-powered technologies can help in detecting and mitigating risks in today’s data-intensive environment.
We at Coforge, combine our domain knowledge with expertise in Big Data, EDA, APIs, and Low code platforms to provide differentiated value. Using innovative technologies we can forecast, predict, and identify fraud in new business and claims specifically in personal lines leveraging large volumes of data.
- In the case of Loan or Credit Risk – Lending is a key revenue mechanism of a financial institution but also poses one of the biggest areas of risk. With AI and ML institutions can effectively measure and manage credit risks. The objective is to minimize potential risks and maximize returns for the firm. The value of any collateral that has been pledged by the borrower can be digitally evaluated on a real-time basis to ascertain whether the loan risk versus collateral value is in the bank’s best interest. Additionally, these technologies can help monitor the credit quality and provide insights for proactive actions that need to be taken in case of any signs of deterioration.
- Unexpected Events and Capital Inadequacy- Financial institutes are leveraging AI and ML to monitor any unexpected risks that need to be considered and factored into product pricing. This helps them keep enough reserve capital to manage any unforeseen risks that may affect functioning and avoid any regulatory violations.
- Operational risks- Manual processes can result in human errors. Fraud or failure of internal governance and control mechanisms are other factors that constitute operational risks. With 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 advancement has increased client expectations, the competitive pressures for financial institutes to stay on top are increasing. This sector has traditionally been the early adopter of technological innovations, and it also brings with it various risks. Downtime, network perimeter breach, and technological obsolescence are some of the complexities faced by these institutes. This can be countered by proactive robust enterprise security solutions and a global security operations centre to monitor risk powdered by AI and ML. Coforge also provides a security risk assessment solution for an end-to-end holistic view of the security posture, vulnerabilities, and compliances.
