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Banking Made Perfect. How is AI changing the game?
AI & Machine Learning in Banking

Emerging technologies are making banking safer, faster, and more reliable. The implications of AI and machine learning are vast, however, most banks are still working to adopt these changing technologies.
According to a survey conducted by the Narrative Science and the National Business Research Institute, about 32 percent of executives using AI technologies like recommendation engines, voice recognition, and predictive analytics believe that it has helped them save money and provide a better experience to customers.
So, how is this technology helping the industry? Let’s find out:

Risk Management and Assessment

Banks can automate credit risk testing which helps mitigate risk since they get access to reports that contain no human error. In addition to this, AI can help banks predict potential issues by studying past history. This helps banks prepare for future problems.
Moreover, machine learning also plays an important role in saving time by analyzing huge amounts of data in minutes. This removes the need for humans to do manual work. Crest Financial, a leading leasing company, used AI on Amazon Web Services and saw an improvement in risk analysis, without delays linked to traditional methods.

Credit Decisions

Data scientists can prepare models to perform tests on customer data to help them make credit decisions. It allows for an accurate and fast assessment of an applicant. Plus, it also helps eliminate bias since machines tend to have higher objectivity than humans. According to this report, AI cut losses by 23 percent annually.
Banks can use data to differentiate between credit-worthy and unworthy customers.

Fraud Prevention

Almost all financial institutions are at risk of fraud. Fortunately, ML and AI have proven to be instrumental in identifying and reducing fraud. Machines reduce the cost to investigate cases and can reduce workload by up to 20 percent.
Machines can be taught to analyze patterns like client behavior, location, and spending to detect anomalies and alert the institution and/or cardholder.
This is very important because such precision cannot be offered by humans since thousands of transactions are completed per second. Machines are now being used to identify suspicious behavior and block transactions that appear fraudulent.
This allows banks to catch fraud in real-time and even reach culprits. Plaid, for example, uses a complex algorithm to provide fraud-detection technology.

Process Automation

Automating mundane and repetitive tasks allow employees to provide better support and services to customers. Robotic Process Automation allows banks to restructure the workforce and remove human error so they can focus on more important tasks and save time.Ernst & Young reported a 50% cost reduction by automating tasks.

Personalized Approach

Ml and AI allow financial institutions to offer a personalized experience to clients. While businesses and consumers look for a low-risk and safe approach, they’re also on the lookout for better options with unique experiences.
This is how banks are able to send reminders to pay bills on time, use special tools, etc. These personalized tools and tips make consumers happier and keep them loyal.

What’s The Scenario Like?

About 80 percent of banks are aware of the potential benefits of AI, according to an OpenText survey. Moreover, according to the same survey, about 75 percent more banks intend to deploy AI in the next few years.
A major reason why so many financial institutions are turning to AI and MI is due to potential cost savings – up to $47 billion in the next 4 years. The truth is that AI in banking isn’t limited to retail services. It helps improve the overall process, which provides long-term benefits.The shift to AI is, however, not easy since banks are having a difficult time finding and retaining AI and MI experts. Data-driven investments are about the hit the trillion dollar mark and it’s definitely the future.

MI and AI Examples in Banking

Some say that chatbots are more hype but they actually make up about 13.5 percent of the AI vendor product offerings in this sector. Wells Fargo introduced a chatbox a few years ago with this simple message:
“AI technology allows us to take an experience that would have required our customers to navigate through several pages on our website, and turn it into a simple conversation in a chat environment. That’s a huge time-saving convenience for busy customers who are already frequent users of Messenger.”
Capital One is using AI to prevent fraud. ENO, the organization’s assistant, sends members a message if their card gets charged twice for an expense. It helps prevent theft and keeps users aware of their financial transactions.
A similar example is COIN, JPMorgan Chase & Co’s tool that automates the processing of data extraction, legal documents, and contract reviews. MI algorithms are able to identify patterns by using image recognition technology. Thanks to the introduction of this bot, the company is able to save about 300,000 labor hours per year.

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