The Role of Artificial Intelligence and Machine Learning in Financial Services
Introduction
Alan Turing, a computer scientist, was the first to experiment on the intelligence of Machines by asking a question “Can Machines Think?” He conducted a test popularly called Turing Test way back in 1950. The Turing Test is conducted by posing the same set of questions to a Machine (Say-A) and a Man (Say-B). If the judge (Say-C) is unable to distinguish the answers given by the Machine from the ones given by the human then it is inferred that the Machine has passed the Turing Test.
A few years back Artificial Intelligence (AI) used to be predominantly a Research field confined to the academics and research laboratory but today with the tremendous growth in the computational power and data analytics, AI has made impact in developing real world applications. Retail, Healthcare, Education, Automobile, Finance name any domain AI and its offshoot Machine Learning (ML) have a significant role to play.
Financial Services in particular has embraced the innovations taking place in the areas of AI and ML to deliver a plethora of services that are automated and which demands less-human intervention. Researchers have broadly identified 3 types of AI and ML driven Financial Services. This blog post primarily focuses on acclimatizing readers on the types of use cases that endorse the way AI and ML are influencing how the future Financial Services are offered.
Use cases of AI and ML in Financial Services
Traditionally Financial Services are offered at three layers Sentiment Indicators, Trading Signals and Anti Money Laundering (AML) and Fraud detection services. Abundant use cases from all three layers have come into play in today’s financial arena. Let us explore few interesting use cases:
- Sentiment Indicators are used by Social Media Organizations to capture Investors sentiments and the same data is shared with Financial organizations for profits.
- Trading Signals are employed by Financial organizations to make decisions based on a huge repository of data which is difficult to comprehend by a human analyst. But the caution while employing such use cases is the data patterns supplied should be clean and accurate. ‘Garbage-in Garbage-out’ principle holds good in ML discipline too.
- AML and Fraud detection services are also built by many Financial Organizations using AI and ML. In addition credit monitoring and risk mitigation services are also implemented.
Typical Applications of AI and ML in Finance Services
Customer-Focused Applications
AI and ML have been already used in Front-Office in a wide variety of applications.
- Credit quality is assessed by employing algorithms working on huge chunks of client data.
- Assessing the risks involved in selling and pricing insurance policies.
- Deployment of Chat-Bots to converse and communicate with the clients.
Example: AI and ML based Credit scoring tools can be used to speed up lending decisions. It also helps in minimizing incremental risk. As Lending has always been based on credit scores of the client. Machine based lending decisions always prove beneficial for the Financial institutions.
Operational or Back-Office Based Applications
AI and ML have been employed by Financial organizations in Back-Offices like:
- Capital optimization in Banks
- Risk Management Applications
- Modelling Trading out of Big Positions
Example: Machine Learning is widely used to create ‘trading robots’ that can self-learn by interacting with the market changes.
Trading and Portfolio Management Applications
AI and machine learning techniques are active areas of research and development for asset managers and trading firms. In addition to significant research and development (R&D), some firms now use machine learning to devise trading and investment strategies. The extent to which AI investment strategies are autonomous or incorporate human oversight varies on a case-by case basis.
Example: Trading organizations use AI and ML to analyze data and improvise the abilities to attract more clients. By analyzing the past trading behavior of a client helps the organization to predict the next order coming from the client. As trading involves terabytes of data Machines/Tools are the obvious choice over human to analyze them
Conclusion
AI has come a long way from the days of Alan Turing who was the first to test — ’Whether Machines can also Think like Humans?’, Claude Shannon coming with a Chess Playing Search tool, Isaac Asimov’s Three laws of Robotics (1950) TO Today’s Poetry writing, Computer Code creating language based task handling Machines developed through deep learning (2020) depicts the way Artificial Intelligence and Machine Learning have evolved. In fact they are changing the way our daily applications behave. Moreover, AI and ML obviously have tremendously impacted the modern day Financial Institutions function.
