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Artificial Intelligence has extensive applications across several domains: from banking to health, to travel, to finance. It continues to gain traction across industries that rely heavily on data which virtually encompasses all industries. The IHS Markit’s “Artificial Intelligence in Banking” report declares that the global cost for artificial intelligence is expected to reach $300 billion by 2030. AI in banking sector is no exception and it is providing remarkable benefits to banking.
A study by PricewaterhouseCooper shows that 52% of the financial services industry is making considerable investments in artificial intelligence. 72% of business decision-makers assume that AI will be the business advantage of the future. Machine learning has become an important fixture in banking, leading to infinite possibilities as it continues to grow and advance.
How is Artificial Intelligence used in Banking?
Artificial Intelligence uses deep learning, predictive analytics, and machine learning for an improved banking experience. Through AI, fraud detection, risk assessment, cost reduction, and enhanced customer experience is achieved. The banking industry also benefits from AI in combating frauds and hacks while adhering to KYC and AML compliance regulations.
How is AI Contributing to the Banking Sector
There are a number of ways artificial intelligence is revolutionizing the banking practices. Fraud detection, seamless customer support, mobile banking, risk management are some of the fields in which AI is contributing in.
For financial institutions like banks, security is one of the topmost priorities as banks are under constant threat of frauds and hacks. Through AI, decreasing rates of false positives, preventing fraud attempts and lessening manual reviews of potential payment frauds is achieved. According to a recent survey, ‘AI Innovation Playbook’ published by PYMNTS in collaboration with Brighterion, 80% of fraud specialists who employ AI-based platforms believe the AI technology helps reduce payment frauds and prevent fraud attempts. 63.6% of financial institutions that utilize AI believe that it is capable of preventing fraud before it happens. This shows the scope of AI for the mitigation of payment frauds. Payment frauds are reduced through AI’s ability to interpret trend-based insights through supervised machine learning, which is then joined with completely new knowledge achieved through unsupervised machine learning. Through the combination of both types of machine learning, it is determined whether a transaction is fraudulent or not.
The following points depict why AI is one of the most used tools to combat payment fraud:
- With the advancements in technology, payment fraud attacks are increasing in complexity. These attacks normally have a digital footprint or sequence which makes them undetectable using predictive models alone. AI plays its role to mitigate these kinds of attacks and provide a security layer to the bank.
- Through the use of AI, payment fraud can be detected swiftly even on a large scale. This is how AI provides an immediate advantage to banks in battling the many risks and methods of fraud.
Through AI’s predictive analytics and machine learning, anomalies in large-scale data sets can be found in seconds.
Along with the number of other advantages of AI in the banking sector, cost reduction is a big one. It is estimated that by 2023, $447 billion will be saved in costs through the use of artificial intelligence. By utilizing AI, banks can cut costs in three key areas:
A huge amount of time is spent in digitizing, identifying and onboarding document templates. Through the automation of the digitization process, banks can reduce the total time spent on this circle. This results in highly improved cycle times and the benefit of redeploying employees to more important projects.
Errors are costly yet unavoidable. A recent survey by Netwrix shows that human error is one of the leading causes of financial data breaches. Through automation in banking systems, errors are lessened to a remarkable degree without an increase in cost. AI systems are much better at handling unstructured data which leads to lower error rates.
Costs of document digitization are huge. Based on the data from IBM, the traditional onboarding process can cost hundreds of millions of dollars. AI tools that are 80% automated and results in 90% accuracy are utilized so that banks are capable of reducing the costs of these onboarding processes. Approximately 30 to 40 percent of the original cost spent on the onboarding process can be saved. The AI-powered onboarding process also results in lessened error rates and greater use of employee labor.
Enhanced Regulatory Compliance
Banks are always under intense pressure from regulatory bodies to enforce the most recent regulations. These regulations are there to protect the banks and customers from fraudulent activities while at the same time, reducing financial crimes like money laundering, tax evasion, and terrorism financing. AI in banking also helps ensure that banks are compliant with the most recent regulations. AI relies on cognitive fraud analytics that watches customer behaviors, track transactions, recognize dubious activities and assess the data of different compliance systems.
Businesses can remain up to date with compliance rules and regulations through the use of AI. AI systems can read compliance requirements and detect any changes in the requirements through deep learning and natural language processing. Through this, banks can remain on top of ever-evolving regulatory requirements and align their own regulations with them. Through technologies like analytics, deep learning, and machine learning, banks can remain compliant with regulations.
Seamless Customer Experience
Customer experience affects every business in the world, including the banking industry. Customer experience directly affects the way people perceive an organization. Especially in the case of banks, people want access to their money 24/7 and they want swift and easy transaction as well. This is where AI chatbots and voice assistants play their role. Chatbots don’t follow any timezone which helps users access them anytime, anywhere in the world.
The most important feature of these chatbots is that they are constantly learning through previous customer interactions which in turn helps enhance them and their customer service. An example of chatbots in banking is the virtual assistant of Bank of America, Erica. Erica constantly sends notifications to their clients, updates users about their credit scores and helps them pay bills and make transactions automatically.
Apart from chatbots, banks are also employing humanoids. Pepper, which is a humanoid developed by SoftBank, is one such example. Through the use of AI, customer experience is enhanced which in turn increases revenue.
When it comes to customer onboarding or granting credit cards to clients, there is always a risk factor associated with it. To mitigate it, a thorough check of the potential customer is needed to authenticate the client. Through the use of AI, Know Your Customer (KYC) checks are done in real-time to identify the clients. Thus, AI offers the chance to save banks millions of dollars due to fraudulent or poor credit applications. Through the use of biometric technology which includes fingerprints, facial recognition scans, iris scans, voice recognition, etc., banks can implement a supplementary layer of security which in turn helps mitigate risks.
Mobile banking is also another application of artificial technology in banking. AI in mobile banking has remarkably revolutionized the concept of banking and customer experience. The core features of mobile banking are 24/7 availability anywhere in the world as well as providing more opportunities for the bank staff to concentrate on more complex issues. According to a survey by the National Business Research Institute, 32% of financial institutions are already using AI tools like predictive analytics, voice recognition, and recommendation engines to provide a more personalized touch to the customers.
The impact of artificial intelligence and machine learning in the banking sector runs deep. As we continue to become comfortable with the existing AI applications, it will continue to enhance and grow savvier. The banking industry is seeing a number of innovations due to artificial intelligence and machine learning and in the upcoming years, these innovations will continue to progress.