The AI adoption into financial services: Case Study


The financial services industry, the largest earning sector in the world is undergoing significant changes and disruptions. Technology has enabled this industry to improve its operational efficiencies and also the way they interact with their customers thereby enhancing the quality of the service provided and customer satisfaction. The most recent development is the application of Machine Learning and Artificial Intelligence in the financial services industry. Companies are adopting this fastly to innovate and gain a competitive edge in the market place.

The financial services industry uses analytics to extract useful insights for their investment decisions, generation of business models, etc. But the major challenge lies in managing and organizing the data collected. The adoption of AI can significantly help companies in leveraging cost savings, efficiencies, trend analysis thereby improving performance and prompt decision making. AI and Machine Learning are expected to improve the way the financial services industry operates. According to Accenture, the leading professional services company, AI would help the bottom line for financial services companies globally to increase their productivity and cost savings by $140 million by 2025. In addition to the algorithmic trading, that uses a computer program that follows a set of instructions called an algorithm to place a trade deal there are four other ways the financial services industry is embracing AI.

  • Customer engagement: Financial companies are using AI to reduce the time required for the customer identification process to a matter of just a few minutes which otherwise was done manually. Also, this can be used to enhance the customer experience by providing smooth round the clock customer interactions. AI-backed chatbots have also been introduced to manage customer queries and requests.
  • Fraud and risk management: The changing regulatory and governance criteria always challenge these financial sectors. The adoption of AI, which can learn and follow these changes reduces the burden on humans. The current systems also generate a lot of false positives which are reviewed manually. Machine learning can help to reduce this by proper interpretation and improving the quality of screening and at the same time saving the cost to the company.
  • Transforming the deal process: The deal flow search engines which are AI-driven enhance the efficiency of the deal flow by automating the tasks which was earlier done manually and took a lot of the analyst’s time. It can also provide insights and changing trends with real-time data.
  • Due diligence process: AI platform can quickly do a check on the numerous supplier, employment, and customer contracts which when done manually require manual labor. Also, it can help in the screening of the companies and employees and raising alerts for any issues identified.


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