Table of Contents Hide
- Top Areas Of AI’s Impact In The Banking Sector
- Other Areas Of Impact
- How Is Artificial Intelligence Transforming Customer Experience Quotient?
The 21st century is the age of AI. All social media platforms use artificial intelligence to serve better feeds, navigation apps use AI to suggest better routes, and most importantly, governments have started using AI for quick facial recognition. In this race of using AI to give better customer experiences, the banking sector is also leading the way. In the past few years, the sector has adopted AI-led banking software solutions to make operations faster and accurate. From general inquiries and onboarding to security, covenant tracking and portfolio management, AI has entered into every conceivable finance and banking sector.
By leveraging AI, banks are better positioned to handle front-end and back-end operations and unleash profitable outcomes. Cognitive technologies with AI integrations are proving effective against money laundering, financial fraud, and phishing attacks. With AI-led security, banks are delivering seamless services that enhance customer experiences.
In this article, we are going to see how banking sectors and financial institutions are using AI-based tools creatively while still working under the existing regulatory compliance and how AI is bringing a large-scale transformation in the banking sector.
Top Areas Of AI’s Impact In The Banking Sector
The history of AI implementation is only a few years old, and it has already saved hundreds of millions for banks and customers. Not only have banks realized the potential of AI, but they have also started using it on the real ground to optimize their operations. In fact, operational optimization is one of the major sources of profitability for banks.
Many investment banks such as Goldman Sachs are using AI to reduce their banking operations cost. They are now using software robotics in more than 500 business processes. Today, millions of banking transactions are done by smart robots with AI modules, which has sharply raised productivity.
Let us see the major areas where AI is exercising maximum impact:
The biggest concerns of banks today are related to cyber-security and preventing continuous cyber-attacks. Today, the biggest bank robberies are not conducted by masked robbers but people with malicious intentions and skills in cyber-systems. Poorly encrypted data, compromised endpoint devices, third-party software loopholes, and customer’s cyber errors can cause siphoning off millions of dollars in a few seconds.
Modern banks are using AI-led banking software solutions that provide increased security against all potential cyber threats to prevent cyber-attacks, spoofing, and hacking events.
Along with cybersecurity attacks, banks and other financial institutions can now also detect fraud claims with AI.
2. Fraud Detection
A key aspect of ensuring sustainability and profitability in the finance and banking sectors is a reliable fraud detection system. Leading banks of both private and public sectors are now turning to AI to implement fraud detection systems.
Over time, AI-led Machine Learning (ML) algorithms have become precise to detect what type of transaction is actually a fraud. A fraud detection system powered by AI can locate and flag transactions that are suspicious. AI itself can then eliminate such fraud claims or transactions.
AI-powered fraud detection systems can now analyze the shopping patterns of the customer. If a transaction is breaking this pattern, the system flags it as possible fraud or theft from the customer’s bank account. AI-banking tools can also match the information of the clearing cheque, such as the cheque number, cheque date, payee name, account number, and amount. If this information does not match the pattern of cheques previously authorized and issued by the issuer, it is flagged as possible fraud.
Earlier, insurance companies had to manually detect whether a claim is a fraud or not, but now it can be done within seconds by AI.
For instance, AKSigorta, one of Europe’s largest insurance companies, uses AI-enabled advanced predictive analytics to check whether a claim is genuine or fraudulent. Within 8 seconds, its AI can tell if an investigation is required on the claim or not.
3. Trading And Loan Decisions
Banks are also using AI-powered solutions to strengthen their risk management strategies. Large data sets analyzed with AI tools have enabled banks to know the best possible strategies for portfolio management. Moving one step further, AI assistants can now also handle trading decisions. Financial trading companies use AI solutions to conduct faster and accurate trading transactions.
The best part here is that these custom AI assistants are actually more effective than humans in making informed trading decisions. They can also detect new profitable trades that are hard to detect otherwise. AI also significantly reduces the risk attached to investing in new trades. It has led to more profit for the financial and banking agencies.
For instance, Kensho is a company that provides data analytics and machine intelligence to world leaders of the banking and finance industry like Bank of America, Morgan Stanley, J.P. Morgan. In the aftermath of Brexit, traders were able to predict a drop in the British pound with the help of Kensho’s AI-powered databases. A single big prediction like this can help traders increase their profits by many folds.
Major banking sectors and finance institutions are now using the real-time predictions of AI to reduce credit risks while sanctioning loans. Many platforms use AI to provide underwriting solutions to companies that want to give credit to borrowers with little credit history. By using thousands of data points and predicting creditworthiness accurately, AI reduces credit risks. The loan underwriting process can be hugely improved by using ML models, robotic process automation, and different data sources.
4. Credit Assessment
Before the advent of AI, user’s payment records were used to issue credit scores. People who did not have much credit history used to have problems getting loans. But many financial organizations have solved this problem with AI.
Companies use AI to analyze more data points related to customer’s credit history apart from the general aspects that make credit scores. Banking institutions are using AI applications to analyze data sets such as mobile phone activities and social media groups to issue credit scores accurately. It expedites the lending process.
Earlier, the process of credit assessment was slow and could cover only a certain set of people. But with the use of AI, credit bureaus and other financial institutions can now efficiently know the creditworthiness of people with little or zero credit history. In fact, the AI-backed credit scoring system is so efficient that it can even provide data of people who are unbanked.
5. Cross-Selling Opportunities
A large amount of revenue of digital platforms and online businesses comes from upselling and cross-selling opportunities. AI has now created a similar opportunity for banks and financial institutions.
For instance, a customer who’s in the process of buying a vehicle is also likely to buy vehicle insurance. A financial institution can use this opportunity to offer a customized insurance policy to its customer at the right time. If the customer deems it appropriate, he or she will promptly buy the insurance. This way, the revenue of the financial institution gets increased along with the increase of customer satisfaction.
For instance, Singapore-based DBS bank created cross-selling opportunities that include insurance policies, fixed deposits, mutual funds, and many other products. Powered by AI, the open architecture platform of DBS bank is a now million-dollar product that lets the users access multiple banks’ accounts without compromising security.
Other Areas Of Impact
Risk Management: AI helps in detecting fraudulent transactions, potentially risky accounts, anomalies in transactions and lending, and also forecasts any impending crisis. AI plays a crucial and proactive role in risk management for the banking sector.
Mobile Banking: Mobile banking offers customers the facility to conduct transactions 24/7. Banks can provide personalized services and customized plans via mobile apps using AI preferences. AII-chatbots can provide instant customer grievance redressal and general support.
Data Collection: Banking data is humongous. Millions of transactions are recorded in banks on a daily basis. AI-enabled tools can collect, segregate, and retrieve such data in real-time. Artificial intelligence-backed banking software can also determine the correlation between the collected data and provide meaningful insights for further analysis.
Analysis: Banks are now also analyzing large quantities of data and doing complex mathematical calculations efficiently with the help of AI algorithms for back-testing. AI-based banking applications can understand market models and then suggest the best possible decision according to analyzed data.
How Is Artificial Intelligence Transforming Customer Experience Quotient?
The first interaction of a potential customer with the bank or financial institution is customer onboarding. If this interaction is not easy and seamless, the future relationship with the organization is bound to get affected adversely. In integration with big data points and machine learning, AI is now able to ease the customer onboarding process.
With AI-based banking applications, financial organizations now know the friction points in the customer onboarding process – the processes where customers spend extra time, the pages with the highest abandoning sessions for online banking, etc. Based on this knowledge of customers’ behavior, banks are now able to significantly improve the onboarding process. In a time when customers are provided with so many choices, AI lends a helping hand to create long-term customer relationships.
By improving its customer onboarding with AI and ML, Danish investment bank Saxo Bank increased its customer onboarding to 12x. Earlier, the bank used to get an average of 1,500 new customers per month. But when ML reduced the fiction, this number increased to a whopping 18,000 customers.
Personalized Banking Services
The real interaction between banking institutions and the customers begins when they start using the banking services. Since a large number of transactions are done today on mobile apps or websites, banks can now collect large data from these transactions.
While the customer onboarding experience is generic, the subsequent interactions can be customized for each individual customer based on the collected data. AI can improvise the mobile banking app according to the saving, expenditure, or trading pattern of the customer. For instance, the banking app can assist the customers with budget optimization, or a tailored investment plan can be suggested to them with AI.
Chatbots Led To Faster Customer Interactions And Resolutions
Another good example of enhanced customer experience is chatbots. Today, most major banking sectors and financial institutions have custom chatbots that enhance their customer service operations. Even the first interaction between the customer and the bank is through a chatbot!
Chatbots use machine learning and advanced NLP (Natural Language Processing) to provide customer service that is almost identical to that of human beings. These chatbots can understand the customers’ inquiries and give custom replies to provide the best solution in less time.
In addition to providing solutions to customers’ inquiries, chatbots can now also analyze the customers’ accounts and give them custom suggestions to save money.
The best example to show how custom chatbots solve customer’s queries would be one of the largest banks in the USA, i.e., Bank of America. The bank relies heavily on AI to deliver seamless banking services. BoA has its own AI-powered smart chat assistant that addresses customers’ inquiries and helps them with their regular banking services such as check balance, transfer funds, check credit score, general inquiries, etc. It can handle millions of inquiries in a fraction of a second. This way, BoA is capitalizing on AI and machine learning for providing effective banking services.
There is no doubt that AI-based banking solutions have led to a higher speed of operations, a decline in credit losses, and improved accuracy, and an enhanced customer experience. The list of possible AI use cases in the banking sector is a long one. As the technology behind Artificial Intelligence will evolve, this list will get even longer.
James Mordy is a content writer with the IT research firm GoodFirms. An avid reader, a solo traveler, and a tech geek, he loves to read and write about innovations that transform the business world. His interest areas include the latest technologies, software products, artificial intelligence, and machine learning.