Will artificial intelligence take over your banking job?
By Pathik Gupta
Pathik Gupta, an associate partner and regional head of wealth management for Asia Pacific at McLagan, discusses the future of jobs in the financial services industry.
- Banks have been very good in automating tasks and then actually improving certain tasks through historical analysis
- In artificial intelligence where there is predictive and cognitive capability, certain jobs will start to disappear - jobs where you would not need human intervention
- Jobs that will continue to thrive in banking are those that need skills despite AI, with human qualities of creativity, strategy, adaptability, interpersonal skills and leadership
Digitisation has changed banking forever. Cashless transactions, mobile and online communications, paperless submissions, and automated teller machines (ATMs) have changed the face of banking. Disruption is a norm, and organisations both large incumbents and financial technology (fintech) start-ups are creating new markets and controlling consumer experience like never before. According to the Organisation for Economic Co-operation and Development’s (OECD) projections, 25% of the workforce is in jobs where a high percentage of tasks could be automated. With artificial intelligence (AI) and robotics, much of the talks in banking centres around – Will AI take over my banking job? Will my skills still be relevant in the future?
Components of digitisation in banks
There are three types of digitisation occurring in banks — automation of mundane tasks, historical analysis to improve those tasks, and the third most disruptive is predictive and cognitive analysis.
Banks have been very good in the first two components that is automating tasks, and then actually improving certain tasks through historical analysis. Have they replaced jobs per se? Based on Aon McLagan’s study of 105 banks in Asia over the last five years, we have seen an increase of 35% in the number of information technology (IT) jobs and at the same time reduction of 14% in the number of operation jobs indicating bank’s continuous focus on process automation and optimisation. However, we haven’t witnessed jobs going away, but what we have witnessed is the nature of the jobs has changed. So for example, 15 years ago, the teller who was in a retail bank could do bookkeeping and cash transactions, but now these are all automated, and so the teller now needs to key in certain information or scan a certain code, or assist and educate customers with their online or mobile transactions.
The third component is that in AI, where there is predictive and cognitive capability, and here is where certain jobs will start to disappear - jobs where you would not need human intervention. These jobs will be done by machines, for the machines will know that if certain scenario arises this is how to react. This will be the “if to how” scenarios, which machines will handle themselves. This is where jobs of credit analysts and relationship managers will disappear. For example: The job of the credit analyst or credit underwriter is to support the home loan applications and perform a know your customer (KYC), confirm if the applicant has any defaults, through credit check, credit history, credit validations and approve or reject the application. But with AI, banks can now perform a credit check and analyse, based on historical data and future trends, what is the percentage chance of the potential customer defaulting - and all this in nano-seconds. Robo-advisers, cashless transactions and online and mobile banking have already replaced backend operational roles, relationship and wealth managers, remisiers and reduced the need for branch service staff.
It is not all bad news. In a highly competitive market, banks are turning away from mass marketing towards individualised and relevant marketing targeted to each customer’s profile and banking habits. As banks use predictive analysis to compete and thrive, there is a dearth of data analysts and data scientists who have both the technical know-how and the industry knowledge. You can’t create a predictive model, unless you have the talent to create that predictive model. Another such role is the highly specialised regulatory and compliance functions. The heightened regulatory environment has created a need for more people and accountability on banks to rely not just on systems but also individuals from within the industry who can look at it in a holistic manner. Regulations are more complex to interpret and have different interpretations in different jurisdictions. The role of autobots or automated system therefore becomes difficult. The second reason is repercussion of regulatory breach is so high, both in reputation and costs, to the bank that even though if someone does automate something, the risk of failure may prevent the bank from completely automating these tasks.
AI's bigger role within banks
Jobs that will continue to thrive in banking are those that need skills, despite AI, with human qualities of creativity, strategy, adaptability, interpersonal skills and leadership. Therefore, functions that need human interactions like sales, human resource (HR), and marketing will still be relevant but can expect job reconfiguration. AI could help these functions to be more effective by giving them quicker access to relevant information than ever before. Leadership and strategic roles will continue to see a demand in banking. Leaders need understanding of the digital world, and understanding where the industry and market is shifting and where the demographics and competition are moving, so that they can take timely measures to direct their workforce and the business model of the bank in that direction. As banks continue to reduce costs and increase revenues, AI will play a bigger role in the functioning of the banks, the skills and roles in this environment that will survive and thrive are the ones that can’t be digitised.
Pathik Gupta is an associate partner and regional head of wealth management for Asia Pacific at McLagan. The views expressed herein are strictly of the author.
Keywords: AI, ATM, Financial Technology, Robotics, KYC