Artificial intelligence (AI) and machine learning (ML) have been game-changers for almost every industry in the current era. This includes the financial services industry as well. In this article, we will shed light on the disruption that AI and ML are catalyzing in the financial sector.

Driving innovation in finance with AI and ML

AI is a broad term encompassing several technologies, such as ML. While AI is designed to mimic human intelligence, ML enables computers to find hidden insights without being explicitly programmed where to look. Both AI and ML are driving innovation in the financial industry through a myriad of ways, such as embedded finance.

The factors below discuss how banks, financial institutions, and the insurance (BFSI) sector have considered and incorporated the use of AI and ML in their everyday operations:

  • Availability of big data: The financial sector has access to a vast range of data, such as transactional activities and history, which can provide insights for tailoring recommendations and communications to individual customers’ preferences.
  • Technological Advancements: Devices boasting higher computational power and the availability of cloud technologies allow for the efficient processing of larger datasets.
  • Regulatory and Compliance Requirements: Regulatory bodies worldwide require financial institutions to improve data governance and compliance. With RegTech solutions, a subset of Fintech that focuses on managing regulatory challenges with technology, BFSIs can manage compliance better.
  • Cost Reduction and Efficiency: Many front and back office operations can be automated with the help of AI and ML, such as:
    • Investment management 
    • Portfolio optimization, 
    • Underwriting 
    • Loan processing,
    • Claims processing and settlements
    • Customer support services. 

Use cases of AI and ML for banks, financial institutions, and the insurance industry (BFSIs)

According to the 2021 research report “Money and Machines” by Savanta and Oracle, 85% of business leaders are seeking help from AI.

An integral part of the BFSI sector in the modern-day era, AI and ML are assisting with:

  • Driving operational efficiencies
  • Reducing costs
  • Increasing accuracy in data analysis

Fraud detection and prevention 

Traditional fraud detection methods rely on pre-defined rules and static thresholds to identify suspicious activity. These rules are often based on historical data and expert knowledge, but they can be rigid and prone to false positives or negatives. Moreover, there is a possibility that cybercriminals can recognize patterns and overpass these efforts.

AI and ML-based fraud detection use algorithms to learn and adapt to evolving fraud patterns. They analyze large volumes of data from various sources to identify complex patterns and anomalies that traditional rules-based systems might miss.

Credit risk management

With an increased focus on risk management supervision, BFSIs must come up with reliable models and solutions. AI and ML prove beneficial in this context by determining the creditworthiness of potential borrowers. They achieve this by harnessing existing data and predicting the probability of default. 

Predictive analytics

BFSIs benefit from AI and ML by applying the technologies in activities such as:

  • Revenue forecasting 
  • Risk monitoring 
  • Case management 

The increasing volume of datasets contributes to the improvement of statistical models. This, in turn, reduces the necessity for human intervention.

AI agents

Incorporated with Natural Language Processing (NLP) and built with Large Language Model (LLM) technologies, AI-powered virtual agents optimize customer experiences with 24/7 availability, personalized responses, and minimal error-ridden answers. They can also direct complaints to the relevant departments within the organization.

Insurance underwriting and claims

AI and ML assist in insurance underwriting and claims by analyzing vast data sets to assess risk more precisely, automate processes, and minimize bias. Claims processing is similarly enhanced by AI’s ability to detect fraud, streamline tasks, and ultimately lower costs. 


Investment management companies have largely relied on computers for making trades and statistical models. However, with AI trading software such as Bayesian networks, investment management companies can analyze large volumes of data and make precise predictions about the financial market in real-time.

What does the future hold for AI and ML in the financial services realm?

While BFSIs continue to go digital and implement new ways to process data for informed decision-making, AI and ML will help drive growth in financial services in parallel through personalized customer responses and enhanced efficiency. At Venturedive, we possess sufficient expertise in AI and ML services for fintech to break down data silos so your organization can make use of data in the most efficient manner possible.