Many blogs address the effect of AI in the financial and insurance industries, but many of them focus on far-fetched AI and machine learning concepts that have yet to be proven or implemented in either. The ‘uses’ list below details application approaches or strategies that are now in use, although softly, slowly, and behind the scenes.
The six ways we believe AI should be used in the banking and insurance industries are listed below.
1) Detection of Fraud
One of the most sought-after uses of AI in finance is fraud detection, which is thought to be capable of detecting billions of dollars in illegal transactions. While AI is currently widely used in the financial sector, it is predicted that by the end of 2021 the amount spent on AI in finance, with an emphasis on fraud detection, will have tripled. With 72 percent of company executives seeing fraud as an increasing problem and projections of US$44 billion losses due to fraud in the next five years, this shouldn’t come as a surprise.
2) Evaluation of Investments
Machine Learning’s multi-layered neural networks, which mimic human brain mechanisms, can be used in an investment bank’s securities division, in both equity trading desks and fixed income clearing corporation functions, as well as greatly improving performance and accuracy in core investment banking divisions such as capital markets, corporate and institutional advisory products and services, and merchant banking. This has been observed at companies like ING and Barclays, who are both using AI to help bond traders make faster and more accurate pricing choices, as well as improve payment and trading decisions. To get accurate results from these machines, training data is crucial and Cogito Tech LLC provides high-quality training data.
3) Risk Management
With several now delivering more well-targeted changes to current validation frameworks, the number of AI vendor product offerings that address risk management in the financial sector is slightly under 15% and growing. The significant increases in computation and processing capacity can aid in the management of both structured and unstructured data, allowing computers to evaluate the history of risk situations and detect early warning signals of potential future problems.
4) Detecting Minor Alterations
When it comes to money, a lot of text and crucial information is written down, which is where NLP comes in handy. Measuring textual change and comparing documents is a time-consuming effort for humans, but identifying possible monetary, risk, and market changes in finance is a simple and rapid procedure. NLP is also used to evaluate unstructured information and moderate minor patterns that might have an influence on the financial market.
5) Trading Using Artificial Intelligence
Several companies are employing AI-powered research to find investment ideas and construct portfolios. It is now feasible to use AI for market forecasting with growing accuracy because of the easy examination of accessible data. ‘Trading-bots,’ while still relying on human input and hence not fully autonomous, can now conduct trade agreements based on a set of rules. While trading bots are not purely AI, recent efforts to improve these systems have used AI to evaluate the optimal parameters for a particular strategy or to allow the AI to pick from a variety of methods available.
6) Individualized Banking
Banks that engage in AI and human-machine collaboration may see a 34 percent increase in revenue by 2022, according to a published study. What do you mean by that? Virtual financial advisers, for example, may use prior transactions to proactively personalize responses, and machine learning can create pre-defined questions and answers that are simple to respond to and efficient. These services keep track of prior interactions and can tailor your platform or experience based on that information.
While we are still a long way from AI being the world-beating application we have all seen in movies, it is slowly but steadily being incorporated and utilized in industries. Finance is one area that might undergo significant change in favour of increased personalization, security, and risk detection. It remains to be seen if AI can be a game-changer for the banking and insurance industries, but with experts predicting billions in savings as well as greater personalization and security through integration, it appears that, if done right, it might be very important.