The Role of AI in Lending - Part 1

Emerging out of the shadow of a global pandemic, banks and lending firms are entering into an era of massive digitalization. At the backend, tremendous amount of innovation is happening to address credit losses in the recent past, and to catch up with a global recovery process, featuring a growing credit demand from untapped customer segments in both personal and corporate financing. This is also the first time in history when Artificial Intelligence (AI)-enabled, lending automation solutions are being adopted at such a scale, with accelerated investments in the next-generation lending infrastructure. A survey by Deloitte Insights uncovered that 70% of all financial services firms are using machine learning to predict cash flow events, fine-tune credit scores and detect fraud. Moreover, the global AI fintech market is predicted to grow at a CAGR of 23.37% between 2020 and 2025, as per a report by Mordor Intelligence.


AI Technologies of the Future 

The adoption of AI in lending is being seen as revolutionary as the introduction of ATMs, online banking, and mobile banking. This means AI will not just be an addition but a disruption to the entire digital and physical lending landscape. Some technologies that we will increasingly be used at the front and backend are:

  • Facial-expression analysis for virtual loan officers

  • Machine vision and natural language processing for document scanning and processing 

  • Biometric authentication and authorisation of transactions

  • Machine Learning (ML) for fraud pattern detection

  • Real-time transaction analysis to cover risks


The Evolution of AI in Lending

Enabled by digital lending and the rise of neo-banks, financial services are reimagining customer acquisition, KYC, fraud detection, customer servicing and everything in between. The use of AI, ML, Robotic Process Automation (RPA), Optical Character Recognition (OCR), and other such technologies is revolutionising traditional banking models and affecting cost optimisation, scalability, OPEX, ROI and customer experience across credit decisioning, loan processing, fraud detection and the entire lending cycle. Some of the top areas of impact include:

  • Adoption of AI/ML models sensitive to data change to reduce manual effort

  • Integration of efforts of cross-functional teams through well-integrated AI automation models

  • Lending automation to speed up the lending cycle and bring down costs

  • Data adoption to build automated credit decisioning systems

  • Rule-based underwriting to process higher volumes of applications and wean out frauds

  • Use of digital data networks for a faster loan processing

  • Use of alternate data to reach underbanked and unbanked populations

  • Using third-party APIs (open banking) to create a flexible microservices architecture 

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