Artificial intelligence (AI) is now a firm part of everyday life, but not everyone is aware of how it applies within the banking sector. As digitalization increases, connectivity improves, and datasets become more vast, financial institutions are finding opportunities to scale their enterprises. Over the last decade, the industry has accelerated, with more banks realizing the benefits of AI applications.
Using data, banks can analyze transactions, review customer behavior, and better explore internal operational processes. All of this together is helping to minimize costs while improving customer experiences within the sector. In this post, we will review some of the top use cases for automation within banking.
What do we mean by automation in banking?
Robotic process automation (RPA) is embedded within banking processes. RPA uses bots to automate repetitive tasks, including data entry, invoicing, payments, and other administrative work that is generally manual and time-consuming. Efficiency improves as bots follow the rules within a workflow to complete tasks that a human will assign.
While RPA relieves the manual effort that the banking sector requires, AI takes it to the next level of automation. Unlike RPA, AI does not rely on rules, learns from experience, discovering, and optimizing processes without the need for human intervention. The introduction of AI creates what is known as predictive banking.
Predictive banking uses historical data to forecast future events and trends. Machine learning algorithms process vast volumes of data in real-time, allowing banks to understand what will happen next under the current market conditions. The insight from the machine learning models automatically makes decisions without the requirement for lengthy processes. Advanced forms of AI, called neural networks, will adapt independently based on the data feeding them.
In an industry where risks and costs tend to be high, predictive banking is an incredibly valuable opportunity.
Understanding fraudulent behavior
BNP Paribas works with the specialist AI and machine learning company Dataiku to create models that analyze raw data. Historical transactions geed into a predictive framework, making software for banks to find behavioral patterns to optimize cost and improve efficiencies.
In the case of BNP Paribas, the platform looks to accurately predict the likelihood of a transaction being fraudulent through detecting anomalies in data patterns. It uses information from several sources that feed into a central model, potentially uncovering trends that humans may not otherwise discover.
In a similar strategy, Nordic Danske Bank works with Teradata to predict fraudulent behavior. Before deploying the AI system, the existing rule-based engine could only predict fraud with a 40% accuracy. As many as 95% of cases going through investigation did not show fraudulent activity, creating unnecessary cost and resource time. Within only five months, the AI models from Teradata could discover false positives significantly better than the previous system.
Approving Loans
A loan is a long-term contract obligation between a bank and a customer. For that reason, loans pose one of the most significant risks to an institution. It is not unusual for banks to spend an excessive amount of time to find customers that match the right lending profile, avoiding the potential for costly defaults.
Lenddo utilizes machine learning to predict the creditworthiness of individuals. The insights that the platform provides can be vital in supporting an efficient and low-risk lending process. The Lenddo system focuses on emerging markets, such as where individuals don’t have full credit histories and bank accounts.
Customers have an extensive digital footprint through the websites, apps, and social media they use daily. Every time a customer uses an online service, it creates data, and banks can make use of every attribute to better understand creditworthiness. For example, Lenddo spans 12,000 characteristics from social media, internet browsing, and smartphone data. Putting everything together provides a credit score reflective of future risk, allowing banks to accept over 50% more applications.
Banks can also assess how likely a customer is to default on their loan payments. Firms like Crest Financial use a “no credit needed” scheme to offer companies loans up to $5,000 in real-time. Using an automated machine learning platform, they can predict default rates from historical data and decide creditworthiness on the spot.
Identifying high-risk customers is a valuable tool for loan approval. If a bank can reduce risk while improving the customer experience through fast responses, all stakeholders benefit from the process.
Help with budgeting
From a customer perspective, automation can offer personal guidance for budgeting, predicting the likely financial performance of customers. One example could be checking if bills are high than in previous months and suggesting that the customer reviews their payments. The mobile banking app from Wells Fargo includes these features, which analyze information and notify the customer.
The app incorporates useful features. If a customer buys an airline ticket, a prompt will appear, asking them to set up an account travel plan for the trip. In doing so, the bank will automatically accept transactions from other countries, mitigating the risk of fraudulent transactions requiring investigation.
Marketing performance
Institutions like Citibank use predictive analytics to make automated decisions within their marketing strategy. Machine learning models work through a large volume of data and help to target promotional spending. They identify the right people and the right channel to sell their products at the right time.
Banks are not traditionally associated with marketing activity. However, there is little value in offering a loan to someone who does not need it. If banks can tailor marketing efforts, they reduce costs and optimize the results with careful targeting. First Tennessee Bank reduced marketing costs by 20% in one case study using predictive analytics.
As well as acquisition, firms can use data to predict customer churn. American Express uses a predictive model to map past transactions with customers that previously left. When the profiles of customers share similar traits, American Express can add preventative measures, attempting to improve retention.
Automating KYC and AML with AI
Anti-money laundering (AML) and know your customer (KYC) compliance are two processes that typically take up a lot of time and require a significant amount of data. AI can transform how the banking industry deals with such regulations.
Firstly, AI can evaluate who the high-risk customers are. Machine learning algorithms can analyze patterns in data, providing insights on customers that required enhanced due diligence. The AI framework will combine multiple sources of data, presenting evidence to human teams for further investigation. To complete the process usually takes much massive data analysis, but AI takes this away, leaving humans to focus on complex tasks that require their full attention.
In a Dow Jones and ACAMS survey, half of the alerts from KYC tend to be false positives. Processes wrongly flag customers due to behavior patterns, and much time goes into analyzing them unnecessarily. AI uses additional data points that can mitigate false positives, more intelligently than traditional rule-based platforms.
AI can also detect patterns in the text using natural language processing. As regulation changes, AI-systems can quickly adapt and extract the right information without the need for intensive re-training.
Datamatics provide a case study whereby the automation of KYC processes resulted in a 50% reduction in working hours, a 60% improvement of productivity, and a 50% increase in cost inefficiencies. Meanwhile, the framework is 100% error-free.
Summary
Automation is becoming an essential feature of banking for incumbent institutions to remain competitive. While technology like RPA serves a purpose, AI and data scale that to new heights, allowing banks to operate more efficiently in the modern landscape. When exciting Fintech startups are disrupting the traditional players, it has never been more crucial for banks to innovate.
