According to research done by Business Insider Intelligence, banks will make some $450 billion by 2023 by applying Artificial Intelligence. No wonder, then, that AI is playing an increasingly important role in financial institutions’ roadmap for the coming years – 75% of banks with over $100 billion in assets already have AI strategies in place.
Banks don’t just create AI strategies, but are increasingly using AI and Machine Learning in their day-to-day business. We often work with them on ideation workshops, PoC and solution implementation. As an example, we recently had the opportunity to work with Santander Consumer Bank, running workshops and researching how to use ML to boost the sustainability of loan portfolios. We were able to significantly reduce risk while maintaining the same acceptance rate for extending loans.
Apart from credit risk modelling, there is already an impressive range of use cases for AI in banking, covering everything from customer service to back-office operations. Here is a list of the most common AI solutions in the banking sector:
Customer service automation
- Chatbots– applying chatbots to automate customer service increases customer satisfaction. In fact, most simple issues can be solved entirely without human interference. Behind the scenes, automation significantly reduces customer service workloads.
- Biometric identification enables explicit or unnoticed identity verification within remote channels. This can include voice identity verification in call centers or typing manner verification in online banking.
- Customer 360 view – applying deep learning to customer analytics makes it easier to combine insights from various data sources (e.g. transactions, online banking logs, call center interactions). This helps us better understand a bank’s customers and build personalized recommendations and Intelligent customer assistants, making the business more responsive and efficient.
- Churn prediction – Thanks to accurate AI algorithms, churn probability predictions improve customer retention. This is important as customers often churn without obvious warning signs. Thus, it is difficult to run precisely targeted anti-churn campaigns. On the other hand, retention activities can be expensive, sometimes much more so than the value a potential customer may bring.
- Customer life-time value is often used to understand how valuable a particular relation is and to optimize other activities – for example, by integrating Customer Lifetime Value with a probability-of-churn function to focus retention activities on the most valuable clients.
- New client acquisition – Deep learning is particularly suited to improving remarketing. As with the customer 360 view, it promotes the use of all possible information about a prospective customer. This includes cookies and how the individual has interacted with a website – from time spent to what they hovered over and how far into the site they went. Understanding customer behaviour enables a bank to focus marketing activities on potential customers and show them personalized ads, translating into even 2,5x uplift from advertising activities.
- X-sell – ML techniques can be used to improve the selection of customers targeted for outbound CRM campaigns. They combine the benefits from both the Customer 360 view and advanced probability of purchase predictions. This allows a bank to choose the right customer and the right product to cross-sell. As an example, ML has been shown to improve credit card x-sell by 12,5%.
Credit risk management
- Loan application assessment – Machine Learning can analyze unstructured data (e.g. transaction descriptions) more thoroughly than other techniques and find non-obvious dependencies. ML techniques can also be combined with traditional scoring models to get even better results.
- Fraud detection – ML enables nearly fully automated fraud detection, adapting to individual patterns and changing behaviors. It can be applied in areas where a high volume of events needs to be analyzed in real time, e.g. in card payments. AI can find complex correlations, so even the wildest purchase will make sense to AI. Those it can’t wrap its algorithms around will lead to the detection of fraud.
- Debt collection strategies – AI algorithms can generate a customized communication strategy for each customer. It will adjust the contact channel, recommending script for CC, or propose a schedule.
- Continuous portfolio evaluation – detecting SME clients, whose risk of default has risen. This enables banks to react rapidly and start the recovery process before other creditors do.
- Workflow documentation – classifying incoming emails to go to the appropriate department (sales, complaints, support) and customer segmentation (individual, SME, Corporate) reduces the manual work involved with organizing customer service departments.
- Process automation – including for cash operations, trade finance, credit application processing, accounting processes.
A wide range of ML and AI applications is increasingly being used to solve real business problems in banking. As AI becomes more popular, those applications will become the market standard.
This article was prepared in cooperation with Santander Consumer Bank