Top 7 Data Science Use Cases in Finance
In recent years, the ability of data science and machine learning to cope with a number of principal financial tasks has become an especially important point at issue. Companies want to know more what improvements the technologies bring and how they can reshape their business strategies.
To help you answer these questions, we have prepared a list of data science use cases that have the highest impact on the finance sector. They cover very diverse business aspects from data management to trading strategies, but the common thing for them is the huge prospects to enhance financial solutions.
Risk management is an enormously important area for financial institutions, responsible for company’s security, trustworthiness, and strategic decisions. The approaches to handling risk management have changed significantly over the past years, transforming the nature of finance sector. As never before, machine learning models today define the vectors of business development.
There are many origins from which risks can come, such as competitors, investors, regulators, or company’s customers. Also, risks can differ in importance and potential losses. Therefore, the main steps are identifying, prioritizing, and monitoring risks, which are the perfect tasks for machine learning. With training on the huge amount of customer data, financial lending, and insurance results, algorithms can not only increase the risk scoring models but also enhance cost efficiency and sustainability.
Among the most important applications of data science and artificial intelligence (AI) in risk management is identifying the creditworthiness of potential customers. To establish the appropriate credit amount for a particular customer, companies use machine learning algorithms that can analyze past spending behavior and patterns. This approach is also useful while working with new customers or the ones with a brief credit history.
Although digitalization and automatization of risk management processes in finance are in the early stages, the potential is extremely huge. Financial institutions still need to prepare for this change by automating core financial processes, improving analytical skills of the finance team, and making strategic technology investments. But as soon as the company starts to move in this direction, the profit will not make itself wait.
For financial firms, data is the most important resource. Therefore, efficient data management is a key to business success. Today, there is a massive volume of financial data diversity in structure and volume: from social media activity and mobile interactions to market data and transaction details. Financial specialists often have to work with semi-structured or unstructured data and there is a big challenge to process it manually.
However, it’s obvious for most companies that integrating machine learning techniques to managing process is simply a necessity to extract real intelligence from data. AI tools, in particular, natural language processing, data mining, and text analytics, help to transform data into information contributing in smarter data governance and better business solutions, and as a result – increased profitability. For instance, machine learning algorithms can analyze the influence of some specific financial trends and market developments by learning from customers financial historical data. Finally, these techniques can be used to generate automated reports.
Analytics is now at the core of financial services. Special attention deserves predictive analytics that reveals patterns in the data that foresee the future event that can be acted upon now. Through understanding social media, news trends, and other data sources these sophisticated analytics conquered the main applications such as predicting prices and customers lifetime value, future life events, anticipated churn, and the stock market moves.
