Artificial Intelligence (AI) is one technology that's going to change the banking landscape.
Consumers are increasingly turning to digital-only banks. Even the traditional banks have started to offer more online services.
Artificial intelligence streamlines their processes, makes smarter decisions, and manages customer service requests with fewer resources.
It also plays a crucial role in risk management by preventing fraud and fighting money laundering in real-time.
There are many other ways that artificial intelligence can be used to improve the banking industry.
In this blog post, we will explore some common applications of AI in banking.
Key challenges faced by the banking industry
Banks are experiencing a dramatic evolution in their business ecosystems triggered by technological innovations and changing consumer behaviors.
Changing lifestyles, online shopping, big data, and technology advancements have made interactions across organizations and individuals more real-time.
Here are some of the key challenges that are faced by the firms today:
#1: Customer expectations
Customer expectations are rising as more and more people access banking through their smartphones, tablets, laptops, etc.
Digitization is disrupting the way traditional institutions operate their business and provide services.
The emergence of fintech and other big technology companies entering this market has made it more competitive.
Regulatory requirements make the firms constantly change their ways of operations to be compliant.
#5: Staying relevant
Leading firms have started making AI technologies an integral part of their business. This also requires others to be up-to-date with emerging technologies to be competitive and stay relevant.
How AI can help
Using machine learning and deep learning techniques, we can automate tasks and predict outcomes, thus reducing costs and improving service.
The possible fields of application of AI can be broad, depending on the type of data involved, such as data, images, text, etc.
Let us look at some common yet interesting applications of AI technologies in the banking ecosystem.
I have also included a short video that briefly illustrates the common uses of AI in banking.
AI use cases
#1: Process optimization
Banks today are looking for ways to lower operational costs and drive profitability. This involves both front-end and back-end activities.
Claims and mortgage processing are the heavy areas within banking where the application of AI could drive efficiencies.
In claims processing
Claims processing is a part of the consumer's journey - whether it involves claims from car accidents, personal injury, and much more, an insurer will go through all stages to make sure that they are either accepting or rejecting your claim.
Using AI chatbots, the customer provides the details about the claims, which can then be verified against the data stored in the systems. If the claim is found valid, then it can be automatically processed and approved. If it is deemed complex, then those claims will be passed on to the claims officer to process.
Furthermore, by using AI’s natural language processing, we can analyze large volumes of data about policyholders, their habits, etc., to offer more customized products in real-time.
In mortgage processing
Mortgage processing is the processing of mortgage applications. A mortgage processor typically collects the individual's details along with the financial information and verifies that all the needed documentation is in place before the loan file is sent to underwriting.
Using AI’s machine vision, we can digitize all the paper documents, including handwritten loan applications, and extract the contents automatically to classify documents such as payslips, bank statements, and valuation documents and tag them, so we can easily search and find the right documents.
We can then use machine learning models to identify and recognize patterns and make sense of massive data such as bank statements, transaction history, and more.
Further, by using AI’s computer vision and natural language processing, we can automatically compare and verify names, addresses, loan amounts, or terms across various documents. We can find matches and mismatches and notify the loan processor accordingly.
In customer service
The bank's customer service relates to its customers' banking experience. It includes services such as opening an account, deposit and withdrawal of money from an account, ATM card usage, fund transfers between accounts, or accounts at different banks, etc.
AI can help by using custom-built intelligent chatbots to streamline the tedious customer service processes to automatically solve simple customer requests and prioritize tickets based on sentiments for routing to the right team.
Chatbots can also be used to send notifications to consumers, provide balance data, suggest how to save money, provide credit report updates, pay bills, and help customers with simple transactions.
The operations function within a bank is responsible for the smooth running of all aspects of banking operations. This includes customer service operations, call center operations, back-office support, and ATM operations, among others.
AI can help by using machine learning techniques to process large quantities of data quickly, thus increasing efficiency and accuracy and reducing operational costs.
For example, banks can introduce AI-based invoice capture technologies to automate customer invoices and use accessible billing services to remind customers when it's time to make a payment.
#2: Credit risk management
Credit risk management is the process of managing credit exposure, which is a type of financial risk. Credit risk is the possibility that a counterparty will default on their contractual obligations toward you.
Traditionally, if someone is interested in getting a loan, we follow the process of checking their income, bank account history, and repayment ability. Not all of them have this history, such as people entering the workforce, new migrants, etc. As a result, they are often deprived of the loan.
Using AI/ML technologies, we can process thousands of data points from the digital footprints such as social media, browsing behavior, geolocation, and other data to build a credit profile for these borrowers. This way, we will be able to extend the loans to all parts of the society and reduce the overall risk to the loan.
#3: Fraud and money laundering
Banking fraud includes deliberately misusing or dishonestly abusing banking services to obtain funds illegally. Fraud can be committed by customers, bank employees, and third parties, such as criminals who hack into the banking computer systems.
Traditionally, businesses relied on rules-based methods to block fraudulent payments. But with increased digitization and more services being offered, it also gives space to more fraudulent activities.
When it comes to fraud decisions, we need to be super-fast, accurate, efficient, scale quickly, and look for economical ways of detecting fraud.
All of these can only be achieved using AI and ML technologies by running hundreds and thousands of queries and assessing individual customer behavior as it happens with ‘normal’ customer activity to spot anomalies. All of this happens in real-time.
In money laundering
Money laundering in banking means the process of making illegal money or money collected from any kind of crime look like legal money, which can be used to make purchase transactions, and more.
One of the most popular use cases in money laundering is the reduction of false positives in the generation of fraud alerts. Machine learning can be used to solve this problem.
After the screening engine has generated alerts, AI can take the alerts and run another round of scoring - this time using the historical alert generation and processing data - and classify the alerts into different priorities (critical, high, medium, low).
This helps the alert managers to prioritize the volume of alerts raised by the screening system.
AI can also be used for name screening, transaction screening and monitoring. This results in significant operational cost savings and helps the alert managers to focus on genuine alerts.
#4: Trading and wealth management
Trading in banking is buying and selling securities for the banks' account and includes underwriting, dealing, and financing. Wealth management is about providing comprehensive investment advice and services to customers.
Banks are increasingly using machine learning algorithms to devise new trading and investment strategies. AI can create models that predict how long it might take to calculate the ROI for each trade using historical data recorded by human analysts from the investment firm. This can help in improving the overall time taken for the risk prediction process.
Robo-advisor is a type of online wealth management service that provides automated, algorithm-based portfolio management advice.
AI-powered tools can help traders streamline the account opening process and advise them on scaling their portfolios. This includes developing a financial plan, advising on planned home purchases, retirement, protection needs, and state planning, etc.
Trade channel optimization
Trade channel optimization or simply trade solution is about making trading simpler and more efficient for institutions.
It uses AI to get data from the exchange, combine it with other data, and create deep learning-based models that can help traders/portfolio managers to decide when and what to trade.
AI helps in understanding market dynamics more clearly by using natural language processing in volumes, open interest, and various other data points.
Powered by AI and deep learning-based models, trade channel optimization can be used to optimize trade according to price and channel to process the payment more quickly.
A stock market is a public market where buyers and sellers trade company stock.
AI and ML can be utilized in stock markets to gather unbiased information, data crunching, data classification, stock analysis, and pattern recognition.
AI can also be helpful for asset managers and hedge funds. The AI-based software can look for non-obvious connections, news, look-ahead bias, and any other online data that might affect investment decisions and prevent catastrophic losses.
Marketing is an organized way of identifying the needs of the customers and serving them. The organization decides what service it should offer to satisfy the needs of the customers so that they get the maximum out of their purchase.
Using various AI solutions such as transcribe, sentiment analysis, keyword matching, event extractions, and more, we can answer questions such as how can I keep my customers happy? What are people saying about me? Who is interested in my product, and what's happening with my competitors?
Furthermore, smart chatbots can be used to help customers interact with financial companies better. With the help of smart apps, the customers may automatically track their spending, plan their budget, and get accurate saving and investing suggestions.
#6: Security and compliance
Security is about protecting information technology systems from unauthorized access or attacks by hackers and viruses.
AI can find breaches in security, such as analyzing documentation for account registration, detecting issues within accounts, and more.
For cybersecurity, we can use unsupervised ML techniques to establish a ‘pattern of life’ for every user and device within the organization and detect anomalies and legitimate threats.
Also, AI can help with data protection and privacy. Banks store a lot of personal and sensitive data about their customers. Now ML algorithms can be used to sniff through the data, identify where they are stored, whether it is encrypted or not, and then make changes as needed.
The banking system is a highly regulated business, which means that banks have to follow all the rules and regulations set by regulatory bodies.
The main aim of these rules and regulations is to make sure that the money in the bank is safe.
AI and RPA can be used to deliver regulatory change automation in real-time across the enterprise.
AI-based solutions can scan and analyze millions of lines in regulatory content, including legal documents, commentary, guidance, and legal cases, to spot applicable requirements much faster and enable compliance.
In the near future, AI is going to be a major driving force in the banking sector. It will change everything from how the firms operate internally to what they can offer their customers online across devices.
AI is not the future of banking. It is the present.
As more and more data is available, new technologies such as quantum, edge, and cloud computing will continue to transform the market.
Success requires a holistic transformation spanning multiple layers of the organization and pulling together people, processes, and data to work collaboratively.
For these firms, ensuring the adoption of AI technologies across the enterprise is no longer a choice but a strategic imperative.
What do you think? How do you see AI impacting the way banking operates in future years?
Have you used any forms of AI at your bank? Please let us know your thoughts!