By Puja Srivastava, CTO and Co-Founder, Spocto

Businesses and individuals often turn to financial institutions for aid to meet their financial requirements. But due to slow processing time, lack of credit history, and tedious background checks in the country, potential borrowers struggle to avail of loans. Furthermore, the Hazaribagh incident reminds us of lenders’ gruesome loan recovery practices. In the incident, a pregnant woman from Jharkhand was allegedly mowed down by a recovery agent working on behalf of the lender. Such unethical and dire debt collection strategies psychologically restrict potential borrowers from taking credit through formal modes, disrupting the lender-borrower relationship, not to mention the ill effect it has on lenders’ reputation and brand image.

Recent incidents and the coronavirus pandemic have shed light on financial institutions’ (FIs’) infrastructure and working mechanisms, giving impetus 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 the Indian Digital Lending Platform Market to be valued at $731.22 million in 2022 and is anticipated to reach $2507.55 million by 2027, accelerating at a CAGR of 27.95 per cent.

Artificial Intelligence (AI) and machine learning (ML) algorithms help financial institutions automate repetitive & enigmatic manual tasks, saving time and labour expenses. The models help mimic human intelligence, assisting lenders in pondering and analysing borrower data. On the other hand, Big Data provides profound insights from various sources, predicting customer behaviours and creating effective strategies for FIs. Big Data helps lenders make informed decisions and streamlines lending-borrowing and debt-collection operations, thereby playing a pivotal role in protecting borrowers’ interests.

Role of AI and Big Data Analytics in Providing Loans

Artificial Intelligence (AI) and Big Data analytics are helping all industries enhance automation & accuracy, and the financial services sector is no exception. Historically in India, borrowers have been facing biases against characteristics like colour, caste, and gender while determining who gets credit and at what terms. A recent survey shows that about 85 per cent of women entrepreneurs struggled to seek a loan from nationalised banks, and about 60 per cent faced difficulties accessing critical financial services between February 2019 and August 2022.

This is where AI comes into play to eradicate all biases and helps potential borrowers avail credit, as the lending decisions are based on data-driven algorithms instead of human judgments. Other use cases of AI are risk evaluation, data preparation, payment reminders, interest computation, etc. The new-age technologies also add much value to operational risk management, fraud management, and credit management. They help lenders in predicting fraudulent and illegitimate transactions, raising caution in advance.

Traditional banking systems use credit scores as a parameter while approving credit terms. However, Big Data analytics enables potential borrowers to avail loans based on alternate credit scores by analysing critical data points like utility bills payment history, online purchasing patterns, IP address and many other variables that determine socioeconomic behaviour.

Alternate data enables lenders to offer debt to potential borrowers with limited collateral or no credit history. This helps borrowers get loans at favourable terms and low-interest rates and allows lenders to make adjusted returns on their loan book and risk-taking potential. For example, NBFCs provide several types of loans to potential borrowers, including business and unsecured personal loans and auto loans for utility vehicles.

Leveraging AI and Big Data to Streamline Debt Collection and Improve Lender-Borrower Relationship

The AI-powered models and Big Data analytics assist in smooth and maximised debt collection, ensuring quicker liquidation, enhanced net collection, and increased monthly account closures. These technologies have enabled several fintech companies to foster innovation, improve effectiveness, and save time. AI-based solutions enable digital payments and provide single-click payment options, allowing NBFCs, DLAs, and banks to extend credit to the underserved population.

AI-powered chatbots and virtual assistants establish automated contact with debtors to facilitate an effortless debt-collection process. They help lenders personalise the tone of the communication, send payment reminders, and even imitate customer communication style to a certain extent. In order to fast-track debt recovery, lenders can also align Big Data analysis with AI-enabled services to detect customer pain points and mitigate them accordingly. The technologies also equip lenders with a bird’s eye view of borrowers’ overall debt history and uncover crucial insights, streamlining the debt collection process and improving the relationship between borrowers and lenders.

Need of the Hour

Debt collection is one of the biggest challenges faced by every lender. However, automating debt recovery processes, clerical operations and understanding debtor behaviour can help lenders accelerate the process. AI and Big Data analytics have the potential to recast the debt collection mechanism that augments Return on Investment (ROI) and adopt ethical practices to enhance lender-borrower relationships.