The CFO’s role has changed over the years from a number-crunching executive to a C-Suite figurehead, as we see today in leading organizations. She is part of the leadership team and is consulted while framing business strategies and policies.

This blogpost talks about three themes around the role of technology in finance. Let us start by addressing the elephant in the room – artificial intelligence eliminating jobs, including several finance jobs. To set some context, according to a study carried out by two Oxford professors, Carl Benedikt Frey and Michael A. Osborne, in 2013, 47% of all American jobs are going to be automated. Putting this in perspective for finance jobs, below are the probabilities of automation of a few popular finance jobs in the coming years,

• Accounting Manager – 98%
• Chartered Accountant – 95.3%
• Manager, Taxation – 95.3%
• Insurance Underwriter – 66%
• Investment Analyst – 41%
• Regulatory Professionals – 7%

Having said this, however, it is not a cause for alarm. With the advent of artificial intelligence, while certain routine jobs may be automated, new roles will be created. The KPIs of professionals in traditional roles will change – they would evolve to become digital enterprise managers responsible for harnessing the power of the digital wave. The takeaway from this should be that AI will not destroy jobs but would be augmenting jobs. IIM MBA Courses are leading the way here through courses such as the One-year MBA in digital enterprise management offered by IIM Udaipur, focused on equipping students to lead in the current business scenario, which is becoming increasingly digital every day.

I want to move on to specific key technology trends in the world of finance that have sprung up in the recent past – Banking as a Service (BaaS) allows third party organizations to draw off existing banking services through APIs that communicate between banks and third parties. Blockchain, along with its distributed ledger technology, brings in many applications for financial services such as Cross-border Transactions, Trade Finance Platforms, Clearing & Settlements, Digital Identity Verification, Credit Reporting, etc. Similarly, the role of AI and Deep Learning is profound with applications in Investment Management, Customer Service, Claims Processing, Fraud Prevention and Regulatory Compliance.

Alternative Data is making waves in the world of finance and insurance. In the current age, useful data can be collected from areas previously not thought of, such as social media, geo-location data, web searches, transaction data, satellite imagery, sensor data, etc. Moreover, high computing power levels are now made available at low-cost thanks to Cloud Computing. Companies are using such Alternative Data to create their investment and trading strategies.

To cite certain use-cases,

• Twitter sentiment data is used for trading within the broad equity market
• News sentiment data used to trade bonds, equities and currencies
• Consumer transaction data used to trade individual stocks
• Geo-location data used to trade in stocks
• Satellite imagery is used to estimate employee activity and trade individual stocks

With the availability of so much data, we need to ask ourselves, are we using the datasets available to the fullest, and are we investing time in analyzing the relevant datasets? Additionally, if more companies use similar data, then the predictive value or competitive advantage is lost, hence the need to keep looking for new data sources exists.

Lastly, I want to talk about the role of Robotic Process Automation (RPA) in the world of finance. RPA is the use of software bots to automate highly repetitive, routine tasks normally performed by knowledge workers. For a financial services company, many activities can be automated. Few use-cases are stated below (Source – CiGen.com),

• Maintaining Data Consistency – Customers’ details are constantly changing – their names, addresses or credit scores. Software bots can use bank statements as reference points, extract the relevant data and update records.
• Accounting – RPA can be used to significantly minimize errors while saving human employees the effort to do data entry or data gathering.
• Promoting Better Options for Investment – Software robots can better track investment values, despite the possibility of sudden, abrupt changes. Bots can also assess an investor’s portfolio and minimize the inherent risk of investing. Relatedly, RPA tools can serve as financial advisors without their human counterparts’ prohibitive costs.
• Carrying the repetitive burden of financial planning

About the Author

Gautam Govindaraj holds more than 8 years of professional experience in capacities of sales leadership, operations, product management, metrics management and customer success management. He is focussed on digital transformation & strategy, product management and the use of analytics to enable data-driven decision-making. He is currently pursuing an MBA program, specialising in Digital Enterprise Management, from the Indian Institute of Management Udaipur (Batch of 2021). You can connect with him on LinkedIn