Gamechanger – Digital Lending powered by AI

AI is increasingly being used in various areas of fintech including financial services, investment and wealth management, payments, and insurance.

Over the last few years, artificial intelligence (AI) has worked its way into every aspect of our lives. The rapid pace of innovation in AI has enabled applications in diverse fields such as computer vision, natural language processing, predictive analytics, and others. It has in turn powered breakthrough innovations in pretty much all sectors including social media, gaming, medicine, logistics, mobility, marketing & BFSI. The adoption of AI can help automate repetitive tasks, make predictions and decisions by analyzing large volumes of data, and improve productivity and efficiency in general.

AI is increasingly being used in various areas of fintech including financial services, investment and wealth management, payments, and insurance. Some of the most significant applications of AI in the financial industry include – risk management, fraud detection, digital lending, automation of compliance, customer service, etc. It has a huge potential to bring more financial inclusion and access to affordable financial services to the underserved and underbaked population.

Digital lending refers to the process of obtaining a loan through an online platform rather than a traditional brick-and-mortar bank. It has seen a rise in recent years, especially in India, because of smartphone penetration, availability of cheaper mobile data, and advancement in technologies such as artificial intelligence. Some of the most common use cases of AI in digital lending are –

1. Underwriting and credit risk assessment – We can use machine learning models to analyze a customer holistically to predict their creditworthiness and risk of default. Instead of a human underwriter looking at a customer file and making a decision incorporating his/her own bias, an automated AI algorithm can look at a larger set of data including credit history, bank statements, employment history, telephony data, social network and make an “unbiased decision”. These algorithms can also help in automating the overall underwriting processes thus further improving efficiency.

2. Customer origination and propensity – The overall customer origination process can be made much more cost-effective and efficient if we are able to predict the propensity of any potential customer to take out a loan. Similar to underwriting, predictive machine learning models can help us identify the customers who have a higher propensity to take loans in a given time period. We can also use natural language processing models to analyze unstructured data including social media posts to gain insight into a borrower’s financial condition, behavior, and preferences. These models can help an organization direct its marketing and customer service efforts in a targeted manner.

3. Fraud detection – AI models can help in anomaly detection and identification of patterns (a high number of applications from a single individual or location, a high number of applications with similar data, etc.) that are associated with fraudulent loan applications. The algorithms can help with both third-party frauds where the fraudster steals someone else’s identity or with first-party frauds where the customer avails a loan without the intention of paying it back.

4. Automation of KYC – AI algorithms can help with the whole gamut of processes involved with Know Your Customer (KYC). We can use AI for identity verification by utilizing facial recognition, other biometric data, and identification documents. We can also perform extensive document analysis to extract and pre-populate the data from customers’ documents and determine the validity of the submitted documents. This allows us to quickly and accurately verify our customer identity further simplifying and automating the whole customer journey.

5. Customer service – AI can also be used to enhance customer service through a multitude of services including chatbots, sentiment analysis, and NLP for understanding customer queries. These AI-based tools can emulate human conversations and provide assistance to customers through voice or text-based interactions, responding quickly to customer queries and improving

customer experience. They can also work in tandem with human customer service agents by suggesting to them the best response based on the query submitted by customers.

6. Personalization – AI can be used to provide a personalized experience to every customer by providing recommendations for the next best action, personalized rewards & marketing campaigns, and tailoring the product to the preferences of individual customers. We can also customize the functionality of the app and user interface by providing personalized navigation, content, and notifications. This can help with user engagement and increase customer satisfaction.

In addition to the above list, one of the biggest changes that AI can bring is in the space of financial inclusion. It can make lending more inclusive by identifying and addressing sources of biases in the overall lending process. It also allows us to analyze alternative data and its utilization in predictive models to include the underbanked and underserved customers. For example – we can use mobile usage patterns, social media activity, utility payments, online payment activity, etc. to generate credit risk scores for customers who lack traditional credit history.

However, it is essential that all AI algorithms and systems are constantly monitored to ensure that they are fair and unbiased. AI models have the potential to not only perpetuate existing biases but have tendency to increase them. This is mainly because either the training data is not diverse enough or there are historical patterns that reflect societal and inherent biases. For example – The majority of the lending in the past has been approved for men and thus a model can inherently create a bias to approve more men than women. In this case, the model may not even have an explicit variable for gender but additional data and features may contain that bias.

One additional consideration to mitigate AI bias is to ensure that data is properly cleaned before use, otherwise, the model may learn from irrelevant data leading to inaccurate predictions. While it might be impossible to guarantee absolute fairness in any complex multi-tiered system, the use of explainable AI, and the establishment of a cooperative relationship between AI and humans will bring us closer to achieving fairer and more inclusive financial products.

In conclusion, AI has the potential to revolutionize digital lending in many ways throughout the customer lifecycle but it is equally important to use AI in a fair and ethical manner to ensure that the new wave of financial products are not only simple to use, and personalized but are also financially inclusive.

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