In this two-part series, co-authored by Sr. Consultant Gaurav Chaudhuri, we will explore the significance and types of AI in the risk management operations space, and then outline a step-by-step guide for a risk compliance leader’s path to AI adoption.

Intelligent machines and automated systems have become pervasive in most aspects of human lives. Artificial intelligence (AI) technology is becoming the pivotal force to usher in a new revolution in the coming decade.

Research highlights the potential of applying deep learning techniques to use cases in the financial services industry as they provide a much greater dimension of solving a practical problem beyond what is possible with traditional analytics techniques.


Financial services organizations are working at a feverish pace to centralize data operations (collection, analysis and dissemination) in order to run different business units including risk management. With the purview of the centralized risk management unit increasing, complex data sets, both structured and un-structured, need to be analyzed quickly to produce actionable information. The scope and quantity of data to be analyzed is growing at an exponential rate. This together with human limitations increase labor cost and errors during analysis and decision making.

Moreover, complying with regulations is difficult as their scope is increasing frequently. Any compliance professional works to keep track of regulations and their impact on the business operations of the financial institution from all relevant regulators. For each impact, they have to ensure that appropriate action or control is in place to adhere to those regulations. The scope of this seemingly straightforward task expands manifold as the number of business lines increase with the growth of the financial institution. Most financial institutions struggle to understand the legal and regulatory requirements of operating globally which results in compliance failures and regulatory penalties both to the organization and to the higher management personnel.


Artificial intelligence has developed at an incredible pace over the last few years, enabled by reduced cost of computing resources and increased availability of vast volumes of data, due to digitization and a pervasive internet. The AI/ML system has the ability to ingest massive quantities of data from diverse sources (both business and social), churn the data into relevant categories and analyze it to produce meaningful information sets to make calculated decisions, both proactive and reactive.  This proves it to be an extremely important and viable candidate for adoption as a decision maker in the risk management operations space.

A study by the FCA and International Institute of Finance concluded that artificial intelligence and machine learning technologies facilitate regulatory compliance more efficiently than existing technologies. The demand in this segment for AI and ML capabilities is high and is estimated to reach $ 6.45 Billion by year 2020, growing at a CAGR of 76% (Frost & Sullivan).

AI agents use machine learning and natural language processing capabilities to ‘read’ unstructured content gathered from scanning news content, social media, relevant legal and regulatory content to develop and evolve its core risk and control engine using the following techniques:

Techniques to develop risk and control engine using AI/ML and language processing abilities to ‘read’ unstructured data

Figure 1: Techniques to develop risk and control engine using AI/ML and language processing abilities to ‘read’ unstructured data

In our next article, we will outline the methodology to adopt AI for a risk and compliance leaders.

Debashis Pradhan

Debashis Pradhan

Partner, Infosys Consulting

Debashis has been with Infosys since 2002 and has nearly 20 years of experience working with Fortune 500 clients in the banking and capital markets space. In his role within our industry vertical, he leads our regulatory, compliance and risk center of excellence for the practice.  Debashis assists financial services firms in successfully executing large enterprise-wide transformation programs in highly complex areas such as AML, anti-fraud, KYC, trade surveillance, employee compliance and enterprise GRC. He has worked with top clients such as Goldman Sachs, Citibank, Fidelity, SunTrust, American Express and Charles Schwab.

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