AI and ML in Banking – The next remarkable differentiators.

Artificial Intelligence?and?Machine Learning?are shaping how banks work and perform their tasks. These technologies are simplifying banking systems.

However, most banks and financial institutions using AI are still experimenting with the?technology and only 12% of them use it at an advanced level to gain a strong competitive advantage, according to 埃森哲 .

Regardless of the stage of AI or ML adoption, global banks are now understanding the benefits of doing so.

What are AI and ML?

AI combines computer science and robust datasets to enable problem solving, and ML, a sub-field of AI, enables software applications to make more accurate predictions after analyzing large volumes of data, reducing risk considerably for banks and financial institutions. With banks facing increased incidence of loan default, credit card fraud, identity theft and money laundering, the need to deploy AI and ML has increased exponentially.

In terms of market opportunity, AI and ML use among banks and financial institutions continues to increase. According to Autonomous Next research, potential cost savings for banks from AI applications are estimated at USD447bn globally by 2023.

Banks increase spending on AI and ML systems

Considering the rate at which new technologies are enabling banks to fast-track customer-services and improve decision making, a large number of banks are planning to invest in AL and ML technologies.

Global annual spending on AI by banks and finance firms is predicted to reach USD64.03bn by 2030. They are expected to spend an extra USD31bn on AI embedded in existing systems by 2025, with fraud management the priority, according to a recent 国际数据公司 report.

AI-/ML-powered systems vs old approaches

AI and ML technologies are far more effective than the obsolete banking systems that are unable to perform most of the modern core business tasks. These innovative technologies improve operational effectiveness and reduce business risks. They are fast, efficient and secure in conducting data analysis,?risk management customer service and credit card fraud detection.

Growth drivers

• Larger IT spending:?larger investments help banks unlock the benefits of AI, helping them stay ahead of the competition. 75% of financial services professionals at banks with over USD100bn in assets are realizing the benefits of AI strategies, compared with 46% at banks with less than USD100bn in assets, according to a UBS Evidence Lab.
• Increased cost saving:?The cost saving that AI and ML generate is increasing their use. Use of these technologies in the?banking sector?is expected to save USD447bn globally by 2023, and up to USD1tn by 2030.
• Favorable policies:?Favorable government policies relating to the implementation of AI and ML in banking are leading to increased adoption of the technologies.
• Need to combat financial crime:?The need to reduce digital crime, now becoming more sophisticated due to money laundering, is pushing banks to adopt AI- and ML-enabled systems and solutions.
• Enhanced customer experience:?Banks are focused on creating novel opportunities for growth and revenue generation by better understanding customer behavior with the help of AI and ML.

How big banks are using AI and ML in their workflows.

• 美国银行 :?In January 2022, it launched CashPro Forecasting, a tool that uses AI and ML technology to predict cash positions more accurately across client accounts.
• The solution was developed in collaboration with a fintech that specializes in applying ML to cash forecasting to help solve the key issue of measuring future cash needs for companies without significant manual effort or costly technology investment.
• 富国银行 :?It is developing a virtual assistant to help it convert more retail banking customers into digital users. The assistant, named Fargo, would help execute tasks including paying bills, sending money, and offering transaction details and budgeting advice. The bank plans to release it in 2022.
• 汇丰 :?In April 2022, the bank leveraged smart analytics to introduce a new Budget feature in the HSBC HK app; this generates personalized insights for customers, enabling them to see spending patterns.
• 花旗 Group:?The bank has developed a solution – Citi Smart Match – that leverages the AI and ML technology of a fintech partner with Citi’s own proprietary assets to create tangible benefits for its customers’ businesses.
• Boosted technology spending by 14% in 2Q 2022 to improve data governance and organization for better informed capital decision making. Citi is to implement a new banking platform with 37 separate loan processing systems consolidated on a single platform along with risk management capabilities. The transformation is to improve governance, processes, enhance policies and leverage technology, strengthening controls.

Conclusion

AI and ML will provide banks an extreme level of precision for managing the vast amounts of information and data acquired from customers, transactions, and other sources, helping them offer a personalized banking.

AI- and ML-based systems and algorithms would also increase protection against fraudulent activity and other risks, reducing operational costs for banks. These technologies are also expected to improve compliance and operational competency.

Several factors such as increasing investment in AI and ML would lead to banks increased adoption of these technologies. This would result in banks reviewing the market, understanding industry best practices, and seeking reliable partners.

This would eventually provide a niche market for consulting,?technology & Market research firms?looking to play a part in the evolving AI and ML landscape.?

For more such Insightful content, feel free to connect with RISHABH BHARDWAJ & Akshaya N.