Though banks don’t create AI strategies, they are increasingly using artificial intelligence and machine learning in their day-to-day business. We frequently work with them on ideation workshops, PoC, and solution implementation. Santander Consumer Bank, for example, is running workshops and researching how to use machine learning to boost the sustainability of loan portfolios.
Besides credit risk modeling, there is already an impressive range of use cases for AI in banking. It covers everything, from customer service to back-office operations. The most common AI solutions in the banking sector are listed below:
Customer service automation
Applying chatbots to automate customer service helps customers to satisfy. Moreover, simple issues can be solved entirely without human interference. In other words, automation significantly reduces customer service workloads.
It enables detailed or unnoticed identity verification within remote channels. It can include voice identity verification in call centers or typing verification in online banking.
Customer 360 view
Using deep learning to customer analytics makes it easier to combine insights from various data sources such as transactions and online banking logs. It helps to understand a bank’s customers better and create personalized recommendations and intelligent customer assistants, making the business more responsive and efficient.
Because of accurate AI algorithms, churn probability predictions improve customer retention. This is crucial as customers frequently stir without obvious warning signs. Therefore, it is challenging to run mainly targeted anti-churn campaigns. On the other side, retention activities can be costly, sometimes much more so than the value a potential customer may bring.
Customer lifetime value
Customers’ lifetime value is often used to analyze how valuable a particular relation is and to optimize other activities, such as by integrating customer lifetime value with a possibility-of-churn function to focus retention activities on the most valuable clients.
New client acquisition
Deep learning is focused on improving remarketing. With the 360 customer view, it promotes the use of all possible information about a customer. It includes cookies and how the person has communicated with a website. Understanding customer behavior on the internet enables a bank to focus on marketing activities on potential customers. It also shows them personalized ads, translating into even a 25x increase from advertising activities.
Machine learning methods can be used to improve the selection of customers targeted for outbound CRM campaigns. They collect and combine the benefits from both the customer 360 view and the advanced probability of purchase predictions. It allows a bank to choose the right customer and the right product to cross-sell. For example, machine learning has been shown to improve credit card x-sell by 12.5%.
Credit risk management
Loan application assessment
Machine learning can process unstructured data like transaction descriptions more thoroughly than other techniques and discover non-obvious dependencies. Machine learning methods can also be combined with traditional scoring models to get better results.
Machine learning thoroughly detects frauds, adapting to individual patterns, and changing behaviors. It can be used in areas where a high volume of events requires to be analyzed in real-time. AI can spot complex correlations; hence the wildest purchase will make sense to AI. Those who cannot wrap their algorithms will drive to the detection of fraud.
Debt collection strategies
Algorithms of AI can generate a customized communication strategy for each customer. Recommending script for CC, or purpose a schedule, it will adjust the contact channel.
Constant portfolio evaluation
Identifying SME clients enables banks to react rapidly and start the recovery process before other creditors do.
Categorizing incoming emails to visit the appropriate department such as sales, and complaints and customer segmentation like an individual, SME, Corporate reduces the work manually involved with organizing customer service departments.
It includes cash operations, trade finance, and credit application processing, and accounting processes.