The Indian Non-Banking Financial Companies (NBFC) sector is witnessing a significant transformation in their day-to-day business transactions. Not just the NBFCs in the upper layer as classified by the RBI's scale-based classification, but the NBFCs in the middle and base layers too are leveraging the power of artificial intelligence (AI) and machine learning (ML) to enhance customer experience and provide optimal financial solutions across diverse customer groups. The strategic adoption of AI and ML is upgrading how NBFCs interact, assess, and serve their customers, resulting in an optimal, efficient, and more personalised financial journey.
In this article, we look at the ubiquitous role AI and ML algorithms play in positively shaping the customer experience and the operations of NBFCs themselves.
STREAMLINING CUSTOMER ONBOARDING
Traditionally, NBFCs have relied on manual processes for customer onboarding, assessments, and loan approvals, leading to lengthy turnaround times for customers. From the organisational side, increased scalability often implies larger teams, larger offices, and increased subjectivity at every step. However, with the deployment of AI and ML, the scenario has changed for the better in the following aspects:
• Automated Document Verification: The use of specialised algorithms to compare data captured via snapshots of identity documents like Aadhar cards, driving licences, etc and the data pulled in via India Stack APIs has significantly accelerated the authenticity of the borrower's identity. The inherent subjectivity of human filtering has given way to confidence intervals of the AI model in use. For example, a lender may decide an 80 per cent match of the selfie clicked by a customer, and his Aadhar biometric is sufficient validation of identity for loan application purposes. Such detailing obviates subjectivity found by a human officer inspecting documents and making decisions.
• Alternative Data Scoring: ML algorithms can analyse non-traditional data sources, such as GST returns, utility bills, employee PF payments, and personal digital footprints on social media (with customer consent), to evaluate creditworthiness. This widens the reach of NBFCs to serve customers who earlier remained unbanked, underbanked, or without sufficient formal financial footprints. ML, thus, has the potential to accelerate financial inclusivity in our country.
• Real-time Decision Making: ML models can process loan applications round the clock, providing faster approvals and promoting customer delight. By using machine learning, sachet loans can be approved within minutes, and the model can be continuously improved for better decision-making.
IMPLICATIONS BEYOND STREAMLINING: AI FOR ROBUST DECISION MAKING AND COLLECTIONS
Deploying ML and AI models, offer benefits extending beyond streamlining processes which are as follows:
• AI-powered Risk Assessment: NBFCs can leverage the power of AI to develop robust and sophisticated BREs, facilitating quick and comprehensive assessment compared to traditional models. AI models can incorporate multiple parameters, including alternative data points, to create a holistic view of the borrower's creditworthiness. This improves loan approval accuracy and empowers NBFCs to make informed decisions on loan terms and interest rates.
• Predictive Analytics for Collections: AI can be trained to analyse financial data of customers to predict potential delinquencies. Using identified behaviour and financial markets; borrowers could be classified into buckets with varying risk-tags allowing NBFCs to take proactive measures, such as personalised communication in the form of awareness calling, due date reminders and collection-route mapping post due dates for improving collection efficiencies.
PERSONALISED FINANCIAL PRODUCTS AND SERVICES
NBFCs are leveraging ML models to gain wider customer insights and are using these to customise their products and services. Such personalisation fosters higher customer satisfaction compared to generic products and cultivates loyalty. More specifically, AI can suggest:
• Product Recommendation Engines: AI/ML frontends can be developed to gather customer inputs and pull core data from bureaus, banks, and other sources to present financial product options that match borrower needs. Recommendations for small personal loans, credit cards, and investment plans are superior to those from traditional financial advisors. An added advantage is that the recommendation models are insulated from personal biases.
• Risk weighed Interest Rates: ML algorithms can personalise interest rates based on the customer's creditworthiness and repayment history. An appropriate risk premium consistent with the riskreward continuum can be baked into the pricing for riskier exposures, thus laying the foundation for a transparent and overall fair lending system.
• Frictionless Upselling and Cross-selling: Traditionally Upsell and Cross Sell via relationship manager led channels is perceived to be intrusive by customers. AI on the other hand can identify potential upselling and cross-selling opportunities by correlating customer financial behaviour and goals. Such tailored need or aspiration fulfilling products, thus allows NBFCs to offer optimal solutions without being intrusive.
TURBO CHARGED CUSTOMER SERVICE WITH AI – CHAT BOTS AND VIRTUAL ASSISTANTS
NBFCs are deploying AI-based chat bots and virtual assistants for providing 24/7 customer service. Some of its benefits are:
• Rapid Responses: Chatbots can address customer FAQs swiftly and efficiently. They can guide customers through basic processes, thus freeing up human agents for more complex inquiries.
• Multilingual Support: AI-powered bots can communicate in multiple Indian languages, effectively catering to a wide audience and thus facilitating financial inclusion.
• Sentiment Analysis: AI can analyse customer interactions and simultaneously gauge the customer's sentiment helping flag potential dissatisfaction triggers. This helps NBFCs proactively address the concern and improve customer satisfaction.
TRANSACTION SECURITY AND TRUST BUILDING
Security and trust form the bedrock of all financial transactions, and this can be ensured by efficient deployment of AI and ML in the following areas:
• Early Fraud Detection and Prevention: ML algorithms are built to analyse transaction patterns and recognise irregularities that may purportedly be fraudulent. Such proactive approach helps reduce overall financial risk.
• Tighter Authentication Checks: AI can be used for setting up multi-factor authentication checks for customers by adding extra layers of security. For-example, logins can be validated by familiar login locations, devices or even networks. Such protection helps keep customer data and funds secure.
THE ROAD AHEAD
The integration of artificial intelligence (AI) and machine learning (ML) in the operations of Non-Banking Financial Companies (NBFCs) is happening rapidly. As technology continues to advance, we can expect innovative applications such as hyperlocal personalisation of products and services, granular risk management models in the assessment and collections space, and even richer customer engagement processes. AI and ML are transforming the customer experience landscape in NBFCs by harnessing these technologies responsibly. NBFCs can build future markets where financial services are truly democratised.
