14 Best practices of AI and ML in banks and financial institutions

14 Best practices of AI and ML

Artificial intelligence and machine learning have been revolutionizing the banking and financial sector for a while. Read this article to find out what exactly these technologies can do for businesses and their clients.

AI- and ML-based technologies enable financial institutions to optimize their processes, prevent fraud and cyberattacks, make smarter decisions, and improve customer satisfaction. The sooner a business integrates such solutions into its workflows, the bigger competitive edge it will have over its rivals. From this article, you’ll get to know about the best practices of AI in the banking and finance sectors. In the next few years, the demand for such tools is only likely to increase.

Risk Assessment

AI-based tools can determine whether a client is eligible for a loan much faster than a human professional. They can provide personalized options to customers and their decisions will be highly accurate. Banks will be able to avoid excessive risks.

Risk Management

Here are a few examples of what ML algorithms can do for the finance sector:

  • Pinpoint trends
  • Identify risks
  • Conserve manpower
  • Ensure better information for future planning

Risk mitigation is an ongoing challenge for the banking and finance industry — and AI enables businesses to make data-driven decisions.

Fraud Detection, Management, and Prevention

ML algorithms can monitor each consumer’s purchasing behavior 24/7/365. As soon as they detect an irregular pattern, both the person and their bank will get an alert. This is the most efficient way of preventing fraud.


AI outperforms human traders thanks to its following abilities:

  • Analyze patterns within large data sets
  • Make instant decisions
  • Never get tired or lose concentration

AI-powered trading can bring considerable income to financial institutions.

Credit Decisions

We’ve already mentioned this aspect before — and now, let’s have a look at it from the customer’s viewpoint. Bank clients don’t need to wait days or even weeks to get their loan applications approved. With AI, customers can be sure the loan process won’t be biased and they can quickly get their money. To predict the loan borrowing behavior of a person, AI can analyze the client’s spending patterns using information from various sources (including the consumer’s smartphone).

Credit Availability

In some regions of our planet, people lack access to conventional loans. But they can use alternative tools to lend and borrow money. Consumers can install an app on their smartphones, and AI can analyze their digital footprints and decide if these individuals should get a loan.

Financial Advisory Services

AI and ML algorithms can provide the following types of advisory services:

  • Analyze a person’s portfolio
  • Gather and process the latest trends
  • Inform the client about the most relevant news in the financial sphere

Unlike human experts, AI and ML always remain unbiased and can quickly process huge databases.

Personalized Banking and Finance Management

AI-powered chatbots and virtual assistants help customers with the following tasks:

  • Check their balance
  • Schedule payments
  • Look up account activity
  • Find answers to questions
  • Receive personalized banking advice

Virtual assistants still can’t fully replace their human counterparts when it comes to non-standard questions and situations. But they’re excellent at simple tasks and they can learn from previous experiences. Unlike chatbots that don’t rely on AI, virtual assistants can interpret human speech and work without templates. Besides, AI-powered chatbots gather customer feedback and a lot of other valuable statistics that financial institutions can analyze to improve their services.

24/7 Customer Interactions

Clients don’t need to call human advisors by phone and wait until they find time to attend to them. AI-based assistants can speak any language and adjust to any time zone. These features enable businesses to achieve the following goals:

  • Boost client satisfaction
  • Minimize customer churn
  • Improve customer retention
  • Expand their client base
  • Scale easily
  • Increase their sales

Consequently, their revenue grows.

Task Automation

If a human professional needs to pull information from applications or review documents, the process might take too much time. They might feel bored or lose focus. AI and ML algorithms can effortlessly cope with this task, allowing people to concentrate on top-priority duties.

Opportunity to Execute Tasks of Any Length

AI turns out to be equally efficient for long-term and short-term projects. Such solutions can scale easily to meet the demands of a rapidly evolving business.

Cyberattacks Prevention

Up to 95% of cloud breaches take place because of human error. AI can boost company security in the following ways:

  • Analyze and determine normal data patterns and trends
  • Reveal anomalies
  • Alert companies of unusual activity

Customers can be sure that financial institutions will keep both their funds and their personal details as safe as possible.

Reduced False Positives and Human Error

94% of IT professionals of the financial services industry confess they aren’t confident that their staff members, consultants, and partners can safely protect customer data. Most data breaches and leaks take place because of human error. AI- and ML-powered solutions can minimize the impact of the human factor and reduce false positives.

Minimization of Expenses

All the practices that were previously listed  also allow financial institutions to achieve the following goals:

  • Automate operations
  • Free up human staff to take on additional responsibilities instead of hiring more personnel
  • Boost client satisfaction thanks to using 24/7 chatbots and virtual assistants
  • Minimize expenses
  • Boost revenues

Of course, their profits will grow only in the long run. The integration of AI- and ML-powered tools requires significant investments. Plus, companies will need to train their staff, hire new professionals, and restructure some of their departments. But the money spent on AI and ML will definitely pay off.

Final Thoughts

Hopefully, you found this article informative and now you better understand the importance of artificial intelligence and machine learning in banking. AI- and ML-powered solutions are indispensable for risk assessment, risk management, fraud detection, management, and prevention. They enable 24/7 customer interactions, provide financial advisory services, and facilitate personalized banking and financial management. They automate repetitive work as well as minimize false positives and human error. AI- and ML-based tools make credits more accessible and help financial institutions make smarter credit decisions. They can carry out tasks of any length, prevent cyberattacks, and trade assets. Ultimately, they let banks increase customer satisfaction as well as save a lot of money.

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