Artificial Intelligence and the Banking Industry

Introduction

Globally, the banking industry holds great importance with regards to trade, investments and several other economic activities. The aim of banking is to develop and integrate systems for people and organizations to effectively store and use their money. Banks also function in provision of loans and other financial activities related to provision of finances. Regarding various activities in the industry, personal remittances were said to have reached $658.06bn at the end of 2020. The global commercial banking market size was about $254.03bn in 2021 and is expected to grow at a CAGR rate of 11.5% to $7404.4bn in 2031. The industry is responsible for numerous employment opportunities worldwide and strides in integrating banking with technology and providing more globalized services are increasing the industry’s capacity for employment. 

The strength of the industry in developing countries has also been used as a yardstick to measure development.  Although about 71% of the global population are said to own a bank account, 1.4 bn people remain unbanked, representing gaps in access to financial services which need to be filled in order to achieve development goals related to economic growth.

Previously, the rise of fintech was considered a competing threat to the standing of traditional banks. Recently however, it has become clear that technology can be harnessed in ways to expand the services of banks and to improve the services offered. This will function to increase accessibility while simultaneously raising profits and revenue for the industry.

Technology can function to improve several areas of importance in the industry. One of such includes operational difficulties which can be improved via automation. Such advances will also function in improving the capabilities of the industry and will no doubt lead to new innovations for seamless experiences. Another important aspect of banking that can be made better by the introduction of more technology is that of security. Although it can be argued that systems themselves are vulnerable to attacks and leaks, better programs can become the best solution for tackling the security challenge of a 21st century industry. 


AI in Banking

Artificial intelligence presents an opportunity for the banking industry to improve the efficiency of existing activities and also holds the potential for creating new services that may increase revenue. Unfortunately, AI implementation in the industry is currently low with only about 1% of banks recognised as AI achievers. Another 75% are in the early stages of developing AI based services. It is expected, however, that if more experts are trained in the intersection of banking and technology and infrastructure is made available with adequate regulations, the involvement of AI in banking will grow over the next few years. If such requirements are met, banks can be expected to save $447bn by using AI applications in various areas of banking:

Mobile Services and Chatbots: Currently, banks generate up to 66% of their revenue from mobile banking services as opposed to traditional banking hall activities. To further improve this, banks are incorporating services into mobile phone software to allow transactions using virtual assistants.  Virtual assistants used by the Bank of America and CapitalOne are great  examples of AI applications in banking. 

Users can also access quick information on banking apps via chatbots. In addition to being available round the clock unlike human customer service centers, chatbots can learn to become more personalized over time and offer more services to users. 

Cybersecurity: Because large amounts of funds are moved constantly through it, the finance industry is rich ground for harboring fraudulent schemes and activities. If AI is to become a successful feature in the industry, it is important not only to ensure that it does not pose any vulnerability but also to apply it in addressing existing security concerns in the industry. To achieve this, banks have already begun to apply AI in cybersecurity with spending on global fraud detection and prevention expected to reach $38.2bn by 2025. Already, large banks such as the Danske bank have been known to develop AI based algorithms that have increased the ability to detect fraud while simultaneously reducing false positives. Phishing schemes and malware have been made detectable using deep learning techniques by researchers at JP Morgan Chase. Other banks  are detecting credit card fraud through analysis of customer behavior and identification of unusual spending patterns and using advanced biometric to prevent identity theft.

Market Trends: With regards to carrying out investments, AI programs have shown much promise in the analysis and forecast of market trends. AI can be useful to banks for suggesting options for investment as well as predicting poor outcomes and opportunities for avoiding them. 

Data Analysis: Large amounts of transactions are carried out through banks and processing these transactions can pose difficulties for human staff. Data integration is an integral part of improving efficiency and also ensuring that information can be quickly retrieved when required. In addition, proper collection of data will help to reduce the likelihood of committing costly errors and allow staff to present more opportunities to customers based on their banking history.

Government Regulations: Due to the sensitive nature of banking activities and their impact on the global economy, governments place strict regulations which banks are expected to implement using compliance teams. These compliance teams can be further improved by using deep learning techniques and NLP to improve decision making and make human staff faster and more efficient.


Conclusion

To achieve success and ensure global integration of AI into the banking industry, there are steps that must be taken by banks to ready the industry for revolutions in technology. Most importantly, AI strategies should be developed in line with organizational goals while also ensuring compliance with industry standards and regulations. Following this, the most important areas for development must be identified and possible solutions tested for feasibility. When development and implementation is complete, continuous monitoring of systems to ensure optimal performance is required to sustain operations. Possible challenges such as data security, in-built bias and the digital divide should be anticipated and addressed to encourage inclusion. Ultimately, companies prepared to invest in software and AI talent will no doubt be rewarded by great strides in the industry for decades to come.  



References

https://data.worldbank.org/topic/7

https://data.worldbank.org/indicator/BX.TRF.PWKR.CD.DT

https://appinventiv.com/blog/ai-in-banking/

https://www.finextra.com/blogposting/20688/use-of-artificial-intelligence-in-banking-world-today

https://thefinancialbrand.com/news/data-analytics-banking/artificial-intelligence-banking/ai-maturity-for-banking-lags-all-other-industries-150480/

https://thefinancialbrand.com/news/data-analytics-banking/artificial-intelligence-banking/six-big-data-and-ai-trends-in-banking-for-2022-cloud-124980/



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