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There is increased pressure on banks and financial services firms from regulators to comply with regulations. Banks and financial firms are facing competition from fintech firms with new business models. Improved customer experience and operational efficiency is the need of the hour. In this direction, technologies such as Artificial Intelligence (AI) and Machine Learning (ML), Distributed Ledger Technologies and Robotic Process Automation (RPA) are enabling increased operational efficiency in business processes to address the needs of banks and financial institutions.

1. Artificial Intelligence

1.1. Robo-advisor

The Robo-advisor diligently gathers client details about their financial health, risk appetite and future investment plans. It then uses that data to advise clients and automatically invest their assets. Some of the best Robo-advisors can initiate onboarding, set up accounts, plan for future investments, provide account services and manage portfolios. After the financial crisis in 2008, Robo-advisors revolutionized the wealth management industry. Deloitte predicts that the current AUM 2.2T as of 2020 and expected to rise to 16T by 2025). Robo-advisor platforms have been largely rule-based and static in nature, but integrating them with AI and ML will hold a greater promise toward the quicker expansion of the former’s global footprint. It will allow the platform to make frequent changes to the portfolio based on market swings.

1.2. Quantitative Investments

Complex algorithms and investment models created by Quants (Quantitative modeler) are used by portfolio managers and traders while making decisions. Quants rely on huge data and complex mathematical models to come up with various strategies. It takes a lot of time and resources to identify recurring patterns. Using AI and ML, quants could easily create, test and validate new trading strategies, optimize portfolios and analyze risks.

2. Robotic Process Automation

In capital markets, the use of RPA has been mostly confined to tasks that are simple, manual, repetitive and prone to human error like client onboarding, KYC etc. Hitherto, we can see that RPA is used more in middle and back office operations like settlements, failed trade management, custody, corporate actions etc., to reduce manual process. For instance, in compliance, users perform lots of manual tasks to identify the cause for a compliance notification. The user has to look into multiple application / reports while performing the task.

Instead, the process can be automated using RPA to investigate the cause for a compliance notification. For example, in the event of a compliance violation, the process of automation of detailed analysis of the cause for notification and immediate steps for issue resolution by front office is a good use case for RPA. This is achieved by removing manual intervention in steps in the lifecycle. Another use case would be with respect to risk teams, which perform various manual tasks to identify the cause of a breach (say VaR/ Portfolio Beta). Using RPA, we can automate these processes by removing manual intervention for breach remediation when a breach occurs, so that the user directly gets to the root cause of a particular breach and works with the front office team to take necessary action to mitigate the risk.

3. Blockchain

Blockchain technology has always had the potential to disrupt the capital market industry. There have been innumerable use cases which have been successfully tested in labs, with more than a billion dollars being spent within the industry. However, most of these use cases never made out into the real world. The reason for lack of industry-wide adaptation of the technology is because of the following reasons

  • Market players like exchanges, clearing houses and transfer agents see this as a threat to their business.
  • There is lack of standardization of various contracts terms in the Over-the-Counter market. The ISDA (International Swaps and Derivatives Association) has been working with industry participants and has come up with a Common Domain Model to standardize events and processes in the trade lifecycle.
  • While major players have setup their own dedicated Blockchain team and are working on use cases, this has essentially been in the form of silos or in small groups. There has not been a real push by the market participants to change the current process.

While we will continue to see more investments and use cases in the future, we will still have to wait for some time till we witness the transformation of the industry using Blockchain technology.

4. Data-Science

Investment banks and management firms possess huge amounts of historical data related to performance, market data, etc. However, the problem with this data is that it is scattered across multiple systems - with several systems storing similar data.

After years of mergers and acquisitions and system upgrades/ changes, some of this data lies unused within the organization. Organizations now look to consolidate this data into a single data warehouse, which can then be used for more in-depth analysis to provide deeper insights into investment strategies, performance, risk management and new investment ideas / product offerings. The major challenge would be to combine this data, which has different structures, models and duplicate information, into a single database to derive meaningful material which could be used to transform different process within the organization.


Given the challenges banks and financial services firms face from various directions, technology can be at the forefront in terms of helping them streamline their operations and serve their customers better. With the post-crisis regulatory frameworks now settling into place, the pace of technology change is the major disruptor for these firms. The key technology trends which will shape the way capital market firms operate in the near future would essentially be Intelligent Automation, Artificial Intelligence and Machine Learning and Blockchain. A data-led and client-centric approach, with open and accessible agile and transparent technologies is necessary to enable banks be future-ready.


About the Author

Nitin Vaid
Senior Consultant

Nitin Vaid is a Senior Consultant with the Banking and Financial Services Practice in Mindtree. He specializes in capital markets technology including Order Management Systems, Data Science and Investment Management.

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