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How AI is transforming the power of data in finance

by Mike Jones in AI, Data Analytics 21/10/2021

AI can leverage a bank’s biggest asset: its data. This can provide traditional finance businesses with a new source of potential income.

It’s clear today and technology matters in the finance industry. The new emerging fintech displays the power of integrating technology with finance. It’s understood that some of the leading businesses such as Monzo and Revolut have succeeded in securing large numbers of customers predominantly because they were one of the first to automate the process of creating a bank account, replacing the traditional time-consuming way of setting up an account.

An automated process like this involves managing data, and as this becomes more advanced, it is often referred to as artificial intelligence (AI). Chatbots represent one of the most common forms of visible AI in finance. WeBank of China reports that nearly 98% of all customer enquiries can be managed via chatbots. Aside from the overall customer experience, AI can enhance finance systems, reduce costs and improve overall margins.

Data represents the biggest factor for conventional businesses to com[pete against fintech. Incumbents are gradually transforming in terms of data and digital technology. Their size and availability of resources provide traditional finance with a significant advantage over fintech and can allow them to catch up relatively fast.

Traditional finance businesses are investing rapidly in AI solutions, with banking scoring the highest of any industry for adopting AI, based on a recent study by GlobalData. The data incumbent finance businesses have gathered through their long years of building a customer base enables a relatively quick closing of the gap if applied with an AI strategy. Once this gap with fintech is closed, the new businesses may not have as clear a competitive edge as before. The Financial Times recently stated that the current performance of fintech banks during the pandemic suggests the concept that leading fintech companies can do anything conventional businesses can do better is diminishing. While fintech has had the initial advantage in terms of technology, it will need to continue innovating and enhance its product offering beyond its existing basic features.

Industry experts believe there is better technology available than apps. The digital-only platform, MyBank provides an example of how AI can generate new options for finance. By 2019 MyBank had launched the 3-1-0 model, a business loan that takes under three minutes to apply and less than a second to approve, with no human intervention required. When used in the right way, AI can reduce the time taken to make a loan approval and at the same time, ensure loans are more effective by lowering the non-performing loan ratios. Other businesses have applied their historical data from existing customers to develop a predictive model and determine the key variables that account for certain factors like missed repayments. Implementing this kind of process is not possible for new banks that lack past information.

Protecting finance data with AI

The more data acquired, the more responsibility you have. Finance data consists of some of the most private and sensitive information. It is therefore critical finance controls this data and AI delivers another layer of protection against potential cyber-attacks.

Several finance services businesses have incorporated machine learning into their security systems. Some have struggled to combat advanced cyber attacks with groups with access to their ML technology and managing their fraud detection rates, with high levels of false-positive alerts daily. Controlling false positives in financial security is a significant issue. Monzo, for example, has come under scrutiny for blocking customer accounts for extended periods because automated software has detected signs of potential criminal activity, and they lack the human staff to manage the backlog.

AI and deep learning systems have reduced this level of false positives and the overall level of fraud detection. These improvements have enabled the finance industry to focus more time on potential fraud, improving its security and enhancing the overall customer experience.

While there may be challenges and concerns with automation, the positives of giving more time to employees due to AI is valuable. In the scenario mentioned, fewer employees focusing on false positives means more satisfied customers and additional staff managing actual cases of fraud.

Whether referred to as fintech or banking, the case of managing money focuses on people and data. If data is handled effectively, people can create accounts, deposit and spend their money easily. When people apply for a loan, the process will determine that the right people are approved, and others declined, and there is transparency for both sides to understand their results.

The most effective data processes available today predominantly include AI technology, and this is the case for the finance industry.