Financial transactions have become more digitised than ever, including the entire lending process – starting from the initial loan application to the final disbursement and even stages after that. But what is the most radical change is the integration of AI-based solutions in the process. As a result, AI has not only made lending more efficient and accessible but has also made it possible to improve customer experience radically.
When it comes to purely digital lending, with the help of sophisticated analytical tools and Artificial Intelligence, it is now possible to gain insights from this data and simplify loan management. Here out outline five ways, India is applying AI in digital lending.
How is Artificial Intelligence Transforming Digital Lending?
Artificial Intelligence (AI) is the differentiator beneath the umbrella of digital technologies, currently disrupting markets. Using AI, lenders can discover customer-behaviour patterns, which can help them stand out from the competitors.
Firms are looking for methods to make their services more effective and profitable for both lenders and borrowers as digital lending develops. The first step is to automate processes.
The second level is when machines take over decision-making, employing powerful machine learning and artificial intelligence to enable real-time, zero-error operations 24 hours a day, seven days a week.
AI enables these fintech companies to attain sustainable business goals and radically change how customers interact with these companies. From faster turnaround times to enhanced security, ease-of-operations and reduced risk, AI is helping both lenders and fintech to scale.
Five Artificial Intelligence Applications in Digital Lending
Indian Fintechs have quickly adopted AI algorithms in their business processes. Many Fintech companies, such as Capital Float, Flexiloans, and Lendingkart, are incorporating artificial intelligence (AI) into their user-facing activities for several reasons, including risk analysis, credit underwriting, wealth management advice, and fraud detection.
Let’s look at some of the critical benefits that AI has brought to the digital lending industry…
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Credit Decision and Underwriting
Credit scoring and underwriting remain some of the most challenging issues and risk sources for most lending operations.
Digital lending businesses typically process significant amounts of customer data that was previously unusable. This provided room for human mistakes, a lengthy loan approval process, and a weak fraud protection system.
Lending companies can make faster and more accurate decisions based on unique scoring technologies. This is due to advanced self-learning algorithms in AI. For example, it used to take days to examine an application and offer a loan to the proper people, it now takes minutes.
Furthermore, lenders can tap into previously underserved or unserved demographics – those who may have once been overlooked.
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Reduced Due Diligence Costs
AI can comprehend billions of data in seconds while also updating the data. This feature allows digital lending organisations to reduce their due diligence costs considerably.
Firms would otherwise have to manually sift through a plethora of records, including the prospective borrower’s credit history, employment and source of income, tax payments, assets, and so on, to determine whether he is eligible for the loan and has the financial means to repay the loan instalments on time.
Furthermore, if the loan is approved, such information must be updated regularly to monitor the buyer from application to distribution. Manually forecasting and revising borrower behaviour is a time-consuming and inefficient operation that is prone to human error.
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Fraud Detection
Loan stacking (clients obtaining several loans from multiple lenders) is widespread in the digital lending industry, among other cybercrimes. AI assists in detecting suspicious and odd behavioural patterns in loan applicants, hence assisting in seeing fraudulent operations.
For example, if a consumer has many loan applications installed from different lenders, AI can recognise this and flag the customer as having the potential to stack loans.
On the other hand, lending organisations can use AI-enabled solutions to keep the applicant’s data safe and prevent security breaches.
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Credit Scoring
Credit scoring is a crucial aspect in determining a customer’s eligibility for a loan and driving the loan book for lenders.
For lenders, AI can enable an alternate credit mechanism. For example, to calculate a credit risk score for a customer, AI can leverage 300-400 data points related to customer behaviour, financial history, income tax history, and other transactions. Data points include customer behaviour on the digital property or its affiliates, customer social profile (social media such as Facebook, LinkedIn, or any other social medium), etc.
These organised and unstructured data can be ingested by AI algorithms, which can then be modelled and output as a credit score. This credit score can be forecasted in real-time or offline, depending on the preference of the lender.
Lenders can use this credit score to contact existing clients to market a pre-approved loan product or contact new prospects. This allows lenders to send targeted messaging to customers and prospects, which helps them expand their lending portfolio. This type of lending method also decreases risk and standardised loan issuance across lenders’ branches/offices.
Precisa is a credit analytics tool that uses bank statement data to present a complete picture of the loan application. It also aids in the identification of data patterns and current trends to aid decision-making.
Thanks to its streamlined approach, fully integrated with the loan origination system, you can gain a new view of your applicants and improve portfolio allocation performance (LOS).
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Customer Acquisition
The purchase experiences of customers for loan products have evolved substantially over time. Customers who are online 24-hours a day leave a large behavioural footprint on the digital assets of lenders or their affiliates. AI can assist lenders in better understanding client behaviour and predicting future business outcomes. Predictions such as whether a consumer intends to acquire a financial product are included.
By utilising clickstream data, search data, and other similar data, AI can predict the purpose of a customer’s purchase. For example, customers might be classified as ‘must reach’, ‘requires more effort,’ or ‘not interested’ based on the results of the AI model.
There may be other categories depending on the needs of the company. Based on this categorisation, lenders can reach out to customers/prospects in a targeted manner, particularly at a very early point of the sales funnel engagement.
Similar to precision marketing, AI can help with the ‘Next Best Offer’ or ‘Next Best Action,’ where lenders can reach out to clients in a targeted manner with customised products to urge them to complete the purchase of lending products.
Takeaway
Artificial Intelligence (AI) has the promise of doing both, thanks to its broad application reach and capacity to make an impact even in the near term.
Many challenges connected with the traditional loan lifecycle, notably the underwriting process, are well suited to AI. It may affect almost every area of the digital lending process, from risk assessment through loan approval and disbursement and asset management. In a fraction of a second, automation can accurately process large volumes of customer data.
Precisa’s bank statement analyser aids digital lenders in obtaining data to conduct an analysis and make informed, calculated judgments when processing the loan. It’s also simple to integrate with existing loan origination software. As a result, there is more transparency and accuracy.
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