The Role of AI in Lending: Top Usecases Explained

Cross-selling to customers

Financial services use AI and ML to make sense of customer data collected at multiple touchpoints in the customer journey. These technologies also help in analysing customer behaviour at the account level through an analysis of the most recent activities. The use of Big Data and Advanced Analytics help in customer segmentation and profiling as per business needs. The data hence gathered helps financial institutions in identifying opportunities for cross-selling and upselling. Customised experiences are then created and curated basis this data for a specific set of customers. 

Extension of the credit limit with credit data profiling 

Predictive data mining models, artificial intelligence-based credit scoring, and automation are replacing complex statistical models of assigning a credit score to a customer. These new technologies make use of structured and unstructured data from a customer’s digital footprints to assign them an internal credit score without any manual effort. In many cases, the credit score is created after considering alternate data sources. This score is more inclusive and reflects dynamic changes in a customer’s behaviour that traditional methods of credit decisioning fall short of addressing. 

Automation-led underwriting

AI models help financial institutions get access to more customer data points for data mining and risk management. Moreover, ML-based models learn continuously by assessing millions of such data points for creditworthiness and with time become apt at giving underwriting decisions with precision and accuracy. Automation speeds up the entire process by freeing underwriters from the manual effort of application processing and credit score evaluation.

Fraud detection

AI and ML help in monitoring financial transactions and user behaviour in real-time. These technologies analyse past transactions of customers to understand the transactional behaviours as well as the past activities on banking information systems. Every time a transaction happens, AI compares that specific transaction against a customer profile/behaviour to compute a risk score, and intimate banking institutions in case of high-risk transactions. Advanced ML, through a unique combination of supervised and unsupervised approaches, also helps banks have a broad spectrum of fraud detection so that cases of false positives are minimised, and effective detection of new and emerging fraud attacks is made possible.

Improved customer experience

By automating manual-intensive tasks, offering end-to-end digital lending journeys, anticipating a customer credit need and issuing personalised credit solutions, lending institutions can improve their customer experience by leaps. A holistic understanding of the customer profile in a dynamic world can be built through data intelligence and advanced analytics. This can simplify customer interactions and build trust through meaningful customer experiences.

Removing human bias using AI

The lending system has traditionally reflected largely prevalent social biases against characteristics like gender, race, sexual orientation and more. Unfortunately, AI-based lending engines can reflect the same biases if the credit decisioning data they infer from is biased and skewed. To reverse past discriminations and to make lending fairer using AI, financial institutions should focus on removing bias from data before data modelling. AI can be used to deliberately spot and alter historic patterns of discrimination over time, creating equitable chances of credit approval for traditionally discriminated sections. Financial institutions can also regularise an AI algorithm to score well on the fairness index. Introducing an AI-driven adversary model is also a great technique to detect hints of bias in the original model and then correct them.

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