The finance industry is harnessing machine learning to lower operational costs and drive profitability. This field involves both front- and back-office activities across multiple institutions.
Cost reduction in insurance
Insurance companies sort through vast sets of data to identify high-risk cases and lower the risk.
Artificial intelligence is applied to functions such as underwriting and claims processing. One of the key technologies here is the application of Natural Language Processing (NLP) that improves decision-making by analyzing large volumes of text and identify key considerations affecting specific claims and actions.
With the rise of digital and IoT (Internet of Things), the points of contacts with the insured will become even more numerous.
Another set of factors can be included in the insurance claim evaluation process. For example, an ongoing AI-powered dialogue through bracelets, sensors, etc. leads to a more comprehensive understanding of the insured.
By collecting and analyzing additional data, insurers are able to analyze the habits of their policyholders and offer highly customized products, adapted in real time to the needs and expectations of their clients.
Operational cost reduction in banking
To maximize their profitability, banks rely heavily on capital optimization.
AI algorithms can be applied to handle large quantities of data to increase efficiency, accuracy, and speed of mathemathical calculations. Using machine learning, banks can find the best combination of the initial margin reducing trades at a given time based on the degree of initial margin reduction in the past under different combinations of those trades.
Banks are also looking to apply AI algorithms to back-testing, in order to assess the overarching risk models.
Using a range of financial settings for back-testing helps to perceive unpredictable shifts in market behaviour and other trends, leading to better decision-making. A similar approach is often applied to stress testing.
Technological advancements can also help financial institutions by introducing a machine learning approach to minimize the trading impact on prices and liquidity, thereby predicting the market impact of specific trades (and the best timing for such trades). This can ultimately lead to minimized impact of trading both into and out of large market positions.