6 Uses of AI, Machine Learning and NLP in Finance and Insurance

There are swathes of blogs covering the impact of AI on both the financial and insurance industries, however, many look at farfetched AI and ML concepts, not yet tested or applied in either. The below list of 'uses' documents application methods or techniques which are currently being implemented, albeit quietly, slowly and behind the scenes.

The below are six ways in which we think AI is best being utilised in both the finance and insurance industries.

1) Fraud Detection

Considered one of the more sought after applications of AI in Finance, it is suggested that the use of AI for fraud detection could detect billions of dollars worth of fraudulent transactions. Whilst AI is already somewhat prevalent in the financial industry, it is expected that by the end of 2021, the amount spent on applying AI in finance with specific focus on fraud detection is set to triple. This shouldn't come as a surprise with 72% of business leaders citing fraud as a growing concern and forecasts of 44 billion USD losses due to fraud in the next five years.

2) Managing Risk

The number of AI vendor product offerings which cover risk management in the financial sector is just under 15% and rising, with many now offering increasingly well-targeted modifications to existing validation frameworks. The vast improvements in compute and processing power can help manage both structured and unstructured data, allowing for algorithms to analyze the history of risk cases and identify early signs of potential future issues.

3) Detecting Subtle Changes

With finance often comes swathes of text and important information in written form, which is where NLP can be extremely useful. Measuring textual change and comparing documents is a painstaking task for humans, but a quick and easy process, classifying potential monetary, risk and market changes in finance. Alongside this, NLP could also be seen to review unstructured content and moderate subtle trends which could impact the financial market.

4) Investment Analysis

Machine Learning's multi-layered neural networks, used to imitate human brain mechanisms, can be deployed in an investment bank's securities division, in both their equity trading desks and fixed income clearing corporation functions, while also greatly enhancing the performances and accuracies in the core investment banking divisions that include capital markets, corporate and institutional advisory products and services, not to mention merchant banking. This has been seen at organisations including ING and Barclays, both leveraging AI to empower bond traders make faster and more accurate pricing decisions and to enhance payment and trade decisions respectively.

5) AI in Trading

Organizations including hedge funds are using AI-powered analysis to source investment ideas and build portfolios. Through the easy analysis of available data, it is now possible to leverage AI for market forecasting with increasing accuracy. Albeit reliant on human interaction, and therefore not autonomous, 'trading-bots' are now able to execute trade deals based on an underlying set of rules. While trading-bots themselves are not strictly AI, efforts to refine these systems in recent years do harness AI to test the best parameters for a given strategy or alternatively enable the AI to choose from multiple strategies available.

6) Personalized Banking

According to Accenture, banks that invest in AI and human-machine collaboration could boost their revenue by 34 percent by 2022. How so? Generally through virtual financial advisors leverage past transactions and proactively personalise response and also through ML formed pre-defined question and answers for easy to answer and efficient queries. These services log previous interactions and can personalise your platform or experience through previous knowledge of previous interactions.

Whilst we are certainly some way off AI being the world-beating application we have all seen in films, it is certainly being integrated and applied in industries, slowly but surely. Finance is one such industry which could see wide-scale change in favour of further personalisation, security and detection of risk. It is yet to be seen if AI can be a game changer for the financial and insurance industries, but with expert predictions of both billions in savings and increased personalisation/security through integration, it seems as though, if done correctly, it could be of huge importance.

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