By Puja Srivastava, CTO and co-founder of Spocto
Businesses and individuals often turn to financial institutions for help in meeting their financial needs. However, due to the slow processing time, lack of credit history and lengthy background checks in the country, prospective borrowers are struggling to avail loans. In addition, the Hazaribagh incident reminds us of the lenders’ cruel loan recovery practices. In the incident, a pregnant Jharkhand woman was allegedly mowed down by a salvage officer working on behalf of the lender. Such unethical and abusive collection strategies psychologically discourage potential borrowers from borrowing in a formal manner, disrupt the lender-borrower relationship, not to mention the negative impact they have on the lender’s reputation and brand image.
Recent incidents and the coronavirus pandemic have shed light on the infrastructure and working mechanisms of financial institutions (FIs) and have given a boost to digital lending. The advent of technology enables digital lending apps (DLAs) and debt collection platforms to offer efficient and valuable services to borrowers and lenders.
Research and Market Report estimates that the Indian digital lending platform market is valued at US$731.22 million in 2022 and is expected to reach US$2507.55 million by 2027, growing at a CAGR of 27.95 percent.
Artificial intelligence (AI) and machine learning (ML) algorithms help financial institutions automate repetitive and puzzling manual tasks, saving time and labor costs. The models help mimic human intelligence and help lenders reason and analyze borrower data. On the other hand, Big Data provides in-depth insights from various sources that predict customer behavior and create effective strategies for FIs. Helping lenders make informed decisions, streamlining lending and collections processes, big data plays a critical role in protecting borrowers’ interests.
Role of AI and Big Data Analytics in Lending
Artificial intelligence (AI) and big data analytics are helping all industries improve automation and accuracy, and the financial services sector is no exception. Historically, borrowers in India have faced prejudice based on characteristics such as race, caste and gender while determining who gets loans and on what terms. A recent survey shows that about 85 percent of women entrepreneurs had difficulty applying for a loan from nationalized banks, and about 60 percent had difficulty accessing essential financial services between February 2019 and August 2022.
This is where AI comes in to remove all bias and help potential borrowers obtain credit as lending decisions are based on data-driven algorithms rather than human judgement. Other use cases of AI are risk assessment, data preparation, payment reminders, interest calculation, etc. The new age technologies also offer great value in operational risk management, fraud management and credit management. They help lenders predict fraudulent and illegitimate transactions and increase caution in advance.
Traditional banking systems use credit scores as parameters when approving loan terms. However, big data analysis enables potential borrowers to take out loans based on alternative credit scores by analyzing critical data points such as utility bill payment history, online buying patterns, IP addresses, and many other variables that make the determine socioeconomic behavior.
Alternative data allows lenders to offer credit to potential borrowers with limited collateral or no credit history. This helps borrowers obtain credit on favorable terms and low interest rates, and allows lenders to earn adjusted returns on their loan book and risk-taking potential. For example, NBFCs offer several types of loans to prospective borrowers, including business and unsecured personal loans, as well as commercial vehicle auto loans.
Leveraging AI and Big Data to streamline debt collection and improve the lender-borrower relationship
The AI-powered models and big data analytics support smooth and maximized debt collection, ensuring faster liquidation, improved net collection and more monthly account closures. These technologies have enabled several fintech companies to foster innovation, improve effectiveness and save time. AI-powered solutions enable digital payments and provide one-click payment options that enable NBFCs, DLAs, and banks to lend to underserved populations.
AI-powered chatbots and virtual assistants automatically connect with debtors to facilitate a hassle-free debt collection process. They help lenders personalize the tone of communication, send payment reminders, and to some extent even mimic customers’ communication style. To expedite debt recovery, lenders can also align big data analytics with AI-powered services to identify customer issues and mitigate them accordingly. The technologies also give lenders a view of borrowers’ overall debt history, providing key insights, streamlining the collections process and improving the borrower-lender relationship.
need of the hour
Debt collection is one of the biggest challenges for any lender. However, automating debt collection processes, clerical work, and understanding debtor behavior can help lenders speed up the process. AI and big data analytics have the potential to redesign the collection mechanism that increases return on investment (ROI) and introduce ethical practices to improve lender-borrower relationships.