How artificial intelligence is transforming the financial sector

Making predictions has always been part of the banking industry. Pricing loans has required an estimate of the risk of a borrower's default by evaluating possible losses, collateral, etc. That is why a deep knowledge and long experience on the part of the bankers was necessary.

Today, it would be difficult to identify a line of business in a bank that does not have multiple predictive analytics needs . As the enormous potential of predictive analytics and machine learning has grown , the need for more data, better modeling capabilities, and the ability to turn data into operational information has exploded.

With the advent of digital transformation, the subdivision has undergone a radical change. All banks want to find new ways to capture and organize data, so they need new tools and techniques to learn from their data and incorporate their capabilities into products, services, customer interactions, and operations.

Chatbots, mobile apps, robotic process automation, and machine learning are some of the forms of artificial intelligence that are gaining traction in the financial industry. In fact, AI is poised to bring the banking industry a potential savings of $ 447 billion by 2023.

But how exactly are these technologies transforming the industry?

1. Help with bank loans

One of the major challenges for bankers is providing credit to the proper extent - to the borrower and for the correct tenure. A small mistake can cause the loan agreement to go bad, resulting in big losses.

Until recently, banks determined an individual's creditworthiness by checking their historical and current earnings, lifestyle expenses, past payment history, etc. Pointless to say, it is a serious process consisting of several reasonable steps and rigorous data compilation.

The use of artificial intelligence can help bankers and financial institutions to create predictive models of people's ability to generate income and ability to pay by analyzing data, thus accelerating credit decision making. It also allows you to identify the accounts that can generate good returns and those that have a high probability of becoming bad loans.

2. Instant personalized support and customer service

In today's digital age, customers expect banks to provide customer support not just in the conventional way (in person), but through digital means.

In this vein, chatbots and live chat software have become the first point of contact for customers seeking customer support. Chatbots are artificial intelligence programs that work according to pre-established rules. The more advanced ones can be integrated with deep learning capabilities that allow them to continually learn from conversations with customers. What are the main advantages? They offer instant personalized support and convenience. In addition, they are ideal for the financial sector, whether for customer service or for sales.

3. Smart management of personal finances

Mobile apps and websites have become popular for personal financial management. Using AI and machine learning, the goal is to compile and analyze all of the user's financial information to organize finances. Furthermore, with predefined logic and an ability to continually learn from interactions, the system can easily automate operations such as transactions. This will result in amplified productivity and reduced errors.

There are also tools that help users quantify their savings and income based on their current spending patterns. Every day we get closer to autonomous finance becoming a reality.

Within autonomous finance, AI-based chatbots help the customer to sign up for a financial service, understand how it works, and even get more details related to security.

4. Risk assessment

Banks and financial institutions are governed by applicable legal regulations within their geographic area. Compliance with these regulations becomes tedious when services are provided in a very large market that spans multiple countries; accounting, risk assessment, ensuring compliance when dealing with securities, insurance products, debt and similar financial products.

With artificial intelligence, reliability in risk assessment can be improved by introducing systematized frameworks that avoid manual errors.

5. Financial fraud

The fight against financial crime, especially money laundering and fraud, is increasingly challenging as criminals become more sophisticated.

Fraud losses appear to be increasing every year, with some estimates claiming worldwide fraud losses of up to $ 200 billion in 2017. And despite the cost, there are still banks that are fighting fraud with outdated systems.

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