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8 Ways AI can Improve Banking Industry

April 27, 2019 | AI

From the days of barter trade to the modern mobile banking era, the finance and payments industry has evolved tremendously over the decades and centuries. Now, technological advances are promising to take the banking industry to a whole new level.

An industry that is significantly different from its predecessors, the banking industry today is run by a world of computers and networks. The bulk of the world’s wealth, whether it belongs to individuals, entities, or nations, is stored in databases. Additionally, the transfer of these funds requires nothing more than exchange over information over a network. Mind-boggling, isn’t it?

As shocking as the above may sound, it is nothing compared to the changes that artificial intelligence (AI) could potentially bring to the banking industry. However, this is nothing to fear about since most of the changes enabled by AI in the banking industry are for the betterment of the industry.

According to the Accenture Banking Technology Vision 2018 survey, the majority of bankers in India believe that AI and humans will work alongside each other in two years’ time or by 2020. So, how is AI aiming to revolutionize the banking industry? We can get a good idea of this by understanding the following 8 ways in which AI can improve the banking industry.

1. Improve Customer Service with Chatbots

Businesses today come across a wave of customer service inquiries on a daily basis. AI-enable chatbots can make it easy to handle these inquiries. Most people today express contempt for banking hours and there’s a genuine reason for that. The banks are never open when you most need them.

The timings and hectic nature of most people’s job or business mean that they only have time during the night or on the weekend to take care of their banking needs. Unfortunately, the bank is closed at this time.  The bank is also closed on holidays when someone could easily run out of money or exceed their transaction limit for the day.

The banking hours are a real pain for customers when you consider that the need to draw money, transfer funds, or take care of another banking need could come up at any hour of the day and any day of the year. However, there’s good news: today we have AI chatbots in banking that serve or engage customers 24/7.

Available round the clock and all through the year, these conversational assistants provide service to customers needing assistance in bank services, bank transactions, or other bank-related tasks without any human intervention.

An example of AI chatbot in banking is Bank of America’s Erica. A digital banking assistant launched by BOA in 2018, Erica interacts with the bank’s customers as if they were talking to a human phone banking officer. Not only can this AI chatbot provide basic banking information such as account and routing numbers, bank balance, and credit score, but it can also complete tasks such as transfer of funds and providing insights and guidance regarding the spending and saving habits of customers. What more can one want from a chatbot?

2. Increase Online Customer Conversion and Improve Experience with Personalized Recommendations

With AI, banks can provide a more personalized service to their customer. This is because AI can provide easy access to information related to customers. Additionally, it can make personalized suggestions based on customer behavior.

Today, a wealth of customer data is stored with banks. This includes detailed demographics, online and offline transactions records, and website analytics. With the help of machine learning (ML) technology, banks can consolidate and analyze data from several, disparate sources or databases to come up with a 360-degree view of the customer. This puts banks in a great position to personalize their interactions, products, and services based on an individual client’s behavior.

AI-processed behavioral data is already being used by some banks to make personalized recommendations to customers. An example of this is Santander, a Spanish multinational commercial bank, that hosted a competition on Kaggle, an ML-based crowdsourcing site, to gather information about customers and then offer customers personalized services based on this data.

3. Improve Regulatory Compliance

A highly regulated industry, not just in the U.S but across the globe, banking requires the financial institutions making up the sector to comply with some regulatory requirements. This protects banks from large-scale defaults by ensuring they have acceptable risk profiles. Another reason for making banks comply with regulatory requirements is to ensure that their customers do not use the banking channels to commit financial crimes such as money laundering and fraud.

For the above reasons, banks are required to uphold customer privacy, know the customer (KYC), stop money laundering, and more. Failure to meet these regulatory requirements results in a significant cost and an even greater liability. To avoid this, banks are increasingly looking to adopt intelligent assistants that offer 24/7 support for keeping track of transactions, monitoring customer behaviors, and auditing information for various regulatory and compliance systems.

One of the pioneers of regulatory technology (regtech) is the UK’s Financial Conduct Authority (FCA). In July last year, the financial regulatory body got together with the Bank of England (BOE) and other financial institutions, to launch the Digital Regulatory Reporting (DRR) project. A project currently in the pilot phase, DDR aims to explore how the use of technology to reduce reliance on human interpretation can help financial organizations to meet regulatory requirements. Another objective of the project is to evaluate the machine-readable reporting benefits.

The overall objective of the project is to help reduce the costs and time involved in the interpretation and implementation of new regulatory requirements through the use of AI technology.

4. Improve and Expedite Fraud Detection

Compared to any human or legacy system, AI-based systems can detect fraud at a much faster rate. They can do this by using machine learning, neural networks, and big data. According to McAfee, a cybersecurity firm, frauds in the financial sector costs the global economy $600 billion each year and a large percentage of these frauds occurs online.

The traditional ways of detecting frauds involve looking at things such as IP addresses, geolocations, the type of items, the amount of purchase, and the difference between shipping and billing addresses. The problem with these traditional methods of detecting fraud is that they often lead to legitimate transactions being declined. This is an opportunity lost for online retailers to generate revenue which can negatively impact their bottom line.

With AI, banks and merchants can analyze the enormous data about customers and transactions available to them at a more granular level. This can help them to differentiate between regular and fraudulent activities.

By allowing the analysis of additional data points, AI can help to lower the frequency of legitimate transactions being flagged while increasing the frequency of legitimate alerts for dubious or fraudulent activity. MasterCard, the electronic payments giant, recently acquired Brighterion, an AI startup. After the acquisition, MasterCard launched a fraud detection system based on machine learning.

The fraud detection system tracks the constantly evolving, real-time conditions to evaluate transactions. This allows Mastercard to update its system constantly and customize its rules according to region and industry to better predict fraudulent transactions. MasterCard has lowered false declines by 50% using the fraud detection system.

5. Expedite Loan Decisions

By using big and machine learning algorithms, AI-based systems are allowing banks and other lenders to make faster and better decisions about loans. Credit scoring systems are the most common and popular way of finding out if someone is eligible for a loan or not. Credit scores reflect your banking history, income, tax payments, and other similar things put together.

While the credit scoring system is of great benefit to people with a well-recorded banking and credit history, it can spell trouble for the millions of underbanked who are not even a part of the digital financial system.

However, artificial intelligence (AI) is helping to change this. AI is helping many companies today to redefine what makes a person ‘creditworthy’. It is also changing how they perform risk assessments for loans. The end result of this is that more people are now legible for loans.

An example of AI’s use in expediting loan decisions is Lenddo, a fintech startup based in Singapore, that uses ‘alternative data’ and machine learning to find out the likelihood of an application repaying their loan. The AI algorithms of the firm penetrate thousands of data points including geolocation data, use of social media accounts, behavioral traits, smartphone information, and subject lines, to find patterns that can tell about the creditworthiness of a customer.

6. Improve Risk Management

With AI systems, banks can analyze the massive amounts of data, both structured and unstructured, that they collect to get intelligent and actionable insights in real-time. The banking industry continues to be dictated by human-based processes despite the digitization that has happened on a large scale in the industry. As a result, there is a great risk of human error in the banking industry.

Why is this such a bad thing? Because it can lead to significant risks and operational costs for a bank. However, there’s good news: we now have AI technology that can minimize, if not eliminate, the chances of human error in the banking processes and the time needed to correct it.

With AI, it is no longer necessary to have human banking employees enter customer data from forms, contracts, and other sources. Today, a wide range of banking workflows are being handled by combining process automation tools with NL, handwriting recognition, and other AI-based technologies, for use in the back-office.

Additionally, banks can implement regulatory and audit control in areas where this wasn’t possible previously, by replacing human-based processes with AI-based automation. Banks can assign their human resources to tasks where they’re more valuable by having intelligent, automated assistants take care of regulatory and audit control processes.

According to Accenture, banks which have implemented AI-based system in their back office for risk management and other purposes are seeing savings of up 25% per year.

7. Streamline Processes and Document Management to Reduce Operational Costs

With AI, banks can easily and automatically enter data into the system, gather information from unstructured sources, and process both printed and handwritten documentation. This frees up employees in the back office to better deliver complicated tasks.

Over the next few years, AI promises to change how documents are stored, managed, and searched. Currently, the only way we can find a document in a computer is to by knowing precisely where it was stored or by entering keywords in a search engine to navigate to document’s location.

AI transforms how we search for documents on a computer, a network, or online. Instead of entering keywords in a search engine, we simply say out aloud to a machine what we’re looking for and it finds the document we need. Not only does the AI-based machine understand you, but it also allows room for error before producing the document. This not only saves time but also helps to increase productivity as the employees have to spend less time looking for documents they need.

In addition to making it easier to manage documents, AI can help to streamline the banking processes. An example of this is the use of bots by JPMorgan, a multinational investment bank based in the U.S, to process internal IT requests. Each AI based bot was expected to handle the work of up to forty full-time human employees. That is massive.

8. Increase Investment Returns

Banks and other financial institutions have been using AI algorithms to improve their investment strategies and enable positive outcomes for themselves and their clients. AI can help banks and other wealth managers to easily and effectively perform wealth and portfolio management.

In a bid to enhance their investment banking research and improve decision making about investments, some banks are using AI-based smart systems. The Dutch bank ING and Swiss Bank UBS are two examples of this. Both banks are currently utilizing AI technology to find untapped opportunities for investment in different markets.

Final Word

In the coming months, years and decades, AI promises to revolutionize every industry that exists today. This includes the banking industry. In the banking industry, AI offers an opportunity to organizations to improve customer service, increase conversions, reduce risks and errors, prevent fraud, improve decision making about loans, streamline processes and document management, and increase investment returns.

At Achievion, we can build AI software and apps powered by neural networks technology to help banks and financial institutions achieve the above-mentioned benefits.

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