Introduction
Last time, we discussed 3 considerations for the responsible adoption of AI in banking, and we hope it helped to allay your concerns about this new-generation tech. In this blog, we can dive a bit deeper into its top 5 applications, starting on the business side of things, an area where most bankers feel a bit more comfortable leveraging tech that is still in the development phase.
Before we get started, just a quick note — your bank’s ability to adopt any of these capabilities will depend on a host of factors, including regulatory compliance, market availability, and resource allocation. So take each with a grain of salt, as some of these recommendations may not work for every use case.
1. Process automation
This is probably the capability that most banks are already dreaming about. AI has the ability to streamline and automate repetitive tasks like account reconciliation, document processing, and compliance reporting, just to name a few. By using advanced robotic process automation (RPA), your bank will be able to automate both rule-based tasks, as well as complex decision-making processes, driven by data insights. This will allow you to reduce error, lower operational costs, and free up your employees to focus on more valuable activities. Just look at your bank tellers — Accenture estimates you could use AI to automate up to 60% of their routine data collection and processing tasks, and that’s just the beginning.
2. Fraud detection/prevention
AI is already able to pick up on things that humans simply can't, and this can go a long way towards the early detection — and hopefully prevention — of fraud. By using advanced machine learning algorithms trained on large sets of historical transaction data, your bank will be able to identify abnormal patterns. And when you couple this with real-time monitoring systems, this becomes even more compelling. By leveraging AI, you’ll start to minimize financial losses and even boost customer trust in the process.
3. Credit risk assessment
Credit scoring takes a long time due to all the underlying factors that must be analyzed, from customer behavior to transaction history and more. But AI will enhance not only the speed of the process, but also the accuracy, due to its ability to speedily review a broader set of data. Thanks to predictive models, your bank will be able to generate more personalized, dynamic risk profiles that will help you make better lending decisions and reduce the risk of defaults.
4. Personalized financial services
AI will make personalization practically effortless, allowing your bank to offer tailored financial services and products at scale. By using recommendation engines to analyze spending habits, income, life events, and more, you’ll be able to suggest the right investment strategy, for example, to the right person at exactly the right time. And that will go a long way towards improving your ability to cross and up-sell.
5. Customer service automation
If you’ve talked to your bank’s call-center staff recently, you’re probably aware that a significant amount of customer queries are fairly simple and fall into a few buckets. That means that AI-powered chatbots are able to leverage natural language processing (NLP) models to engage in conversations with customers, answering common queries and escalating more complex ones to human agents. With these chatbots, your bank will reduce headcount, improve response times, and lower operational costs — and if your AI is sufficiently trained, you’ll even boost customer satisfaction in the process. And that’s not even mentioning the revenue.
Did you know that
McKinsey estimates that AI tech could help deliver up to $1 trillion of additional value to global banks each year, of which revamped customer service is a huge part.
The top 5 customer applications
In the next blog, we’ll discuss the top 5 customer applications of AI. While there may be some overlap between the points we’ve discussed here, that blog will cover a more customer-centric approach to things like fraud protection and personalized financial advice, for example. And after that, don’t miss out on our interview with Chris Shayan, Backbase’s Head of AI, formerly CTO of TechComBank, where he’ll share his 3 tips for adopting this revolutionary tech at your bank.
For more information, check out our Banking Reinvented podcast, where Backbase Founder/CEO Jouk Pleiter dissects similar topics alongside Tim Rutten, EVP/CMO, and other digital leaders. Stay tuned as they chat about everything from progressive modernization to decomposing your bank’s complexity.
