How is AI Redefining Risk Management for Banking Institutions?

New York based CogNext is a next-generation Regtech that allows banks and financial institutions to automate and digitize analytical processes at an enterprise scale. Founded in 2019, the startup is on a mission to build a cloud-native, end user-focused cognitive platform by leveraging its deep domain expertise that will make regulatory compliance & analytical process in financial institutions transparent, interactive, explainable, adaptable, scalable, and cost-effective.

The banking and finance sectors are the early adopters of Artificial Intelligence, but their ability to realize its full potential is still at a nascence. Despite the stage, banks across the globe are leveraging the application of AI for a cost-effective banking process. As per Mckinsey's Global AI Survey, almost 60 per cent of financial-services sector respondents reported that their companies had embedded at least one AI capability. According to a survey conducted by the Bank of England in the UK, Machine Learning (a subset of AI) is being increasingly used in financial services and has passed the initial development phase and is entering more mature deployment stages.

The outstanding credit extended by Banks and other lending institutions globally runs into several trillion dollars, and most of them currently underwritten and monitored using traditional methods. Integrating AI in credit risk management has become an integral part of improving credit workflows' efficiency and productivity. Digital lenders have taken the lead in leveraging AI in credit risk management, and now many traditional banks are investing heavily to upgrade their credit risk systems and set up a modern infrastructure.

AI plays a crucial role in risk management and compliance solutions that makes regulatory calculations and compliance easy-to-manage, scalable, and less costly for large and small financial institutions. It is estimated that AI technologies hold the potential for delivering up to $ 1 trillion of additional value every year for global banking. A large part of this potential would be unlocked by AI-based automation of credit workflows and automated learning from data.

Banks are using AI to reshape the customer experience through enabling round the clock, frictionless customer interactions. However, AI in banking applications isn't limited to only retail banking. The back and middle offices of wholesale, commercial and investment banking, and other financial services are seeing an accelerated adoption of AI.

AI in credit risk management
Credit risk is the single largest risks for a financial and banking institution. It refers to all the possible risks banks take while lending out money to large and small corporations and individuals. The stakes are incredibly high for every lending institution. With outdated or traditional systems, institutions incur huge losses due to unpaid credits. However, a robust credit risk management system integrated with AI technology plays a crucial role in mitigating credit risks and increasing the institutions' cost-effectiveness.

Furthermore, financial and banking institutions take an ample amount of money and time in providing credits. It is mainly due to the process of verifying applicant details physically. Since AI works on data, institutions can leverage the technology to extract meaningful insights from unstructured sources to verify the applicant's authenticity. Consequently, it reduces the processing time of the credit and helps the institution make an informed decision.

AI solutions are aiding banks, and credit lenders make intelligent underwriting decisions by utilizing multiple factors that evaluate traditionally underserved borrowers in the credit decision-making process more accurately. Banking workflows for customer onboarding, fraud and data anomaly detection, marketing, lending and collections, predictive model development, documentation, deployment and monitoring can be automated to a large extent using AI systems.