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The words like data science, machine learning, and artificial intelligence (AI) sound intriguing and even a bit futuristic for business owners and top managers from most modern industries. But this is definitely not the case of finance and banking business areas. The success of companies from this sector heavily depends on the implementation of cutting-edge technologies. And since this market is highly competitive as well as associated with money and investing, the question of whether to take advantage of data science or not is equivalent to the question of whether to stay afloat or sink.
As a broad term, data science refers to an interdisciplinary field aimed at extracting particular knowledge from a vast amount of data by employing mathematics and statistics. To achieve this goal, data science may use many techniques. But when it comes to building actual products that solve real business problems, machine learning, a subset of AI, is the most common option.
In this article, we will discuss top data science use cases in finance. We’ll also try to show you how each of them may help your financial business grow and thrive.
- Data-driven decision making
- Fraud detection
- Personalization
- Advanced customer service
- Data management
- Risk management factor
Data-driven decision making
To succeed in the financial world, traders, investors, and wealth managers have to make the right decision at the right time. Real-time and predictive analytics software takes this process to the next level. Not only does it automate many steps one should take to receive positive outcomes from the trading activity but it also makes such activity much more efficient.
To be more precise, an AI-powered solution for a financial sector can identify typical patterns based on the data gathered from many sources. For instance, it retrieves information from news, books, and even social media like Twitter and Facebook. By applying machine learning algorithms to all such data, the AI-powered solution can make accurate predictions regarding the way the market will behave in the future. Among others, such predictions may be related to stock market moves, trends, risks, and prices. Actually, that’s where such a thing as algorithmic trading comes from.
Fraud detection
Fraud detection is an area in the financial sector where data science and artificial intelligence are used most often. This is because financial institutions are obliged to ensure security to their clients. And even the minor malfunctions, glitches, leaks, and gaps may lead to significant financial losses as well as reputation risks and unscheduled audits by controlling authorities.
Real-time and predictive analytics allows for the substantial enhancement of cybersecurity and fraud detection. With the help of data science, companies providing financial services can effectively prevent fraudulent transactions by identifying unusual financial activity. For example, a fraud detection system may block a particular user if a financial purchase he or she was going to perform constitutes an anomaly due to inconsistent information specified in different payment documents. On top of that, predictive models can foresee manipulations and alert the company’s officers about the risks.
Personalization
Nowadays, businesses in all industries are striving to improve customer experience and the financial sector is not an exception. Machine learning algorithms incorporated in software for financial businesses help such businesses better understand their target audiences. As a result, customers and clients receive more personalized services that naturally leads to stronger brand loyalty and higher profits.
Specifically, AI-powered solution can process not only the basic information a person specifies in a questionnaire but it also can gather data about his or her behavior on a website, likes, comments, historical transactions, feedback, etc. Such advanced client analytics opens a door to numerous marketing opportunities for financial institutions.
But the greatest thing about the application of machine learning in finance is that customer’s interactions with a financial service provider become more contextual as he or she performs more transactions. The reason is simple. Machine learning algorithms need data to provide outcomes. The more data they are fed with the more accurate are the outcomes.
Advanced customer service
Traditional call centers are things of the past. Today, financial enterprises are investing in custom software to automate customer service and take it to the next level. The ultimate goal is to create an opportunity for customers to resolve their issues quickly and effortlessly without the need for intervention by a company’s agent.
One of the most popular solutions in this area is an AI chatbot. The work of such a chatbot is based on natural language processing (NLP). For this reason, it “understands” and can answer most of the customers’ queries almost like a human. To compare, traditional chatbots can process only a limited number of phrases and even the slightest change in wording usually makes them completely useless.
Another great benefit if AI chatbots for financial institutions is that such chatbots can complete simple tasks such as payments without human intervention. On top of that, data-driven customer service software may include claims automation system. Such a system will allow customers to file a claim and get paid in an automated manner. And that is a huge time-saving for both financial enterprises and their clients.
Data management
Financial specialists usually have access to an enormous amount of data. Yet, managing such data is their greatest challenge. The reason is that financial specialists receive information from many different sources and naturally it doesn’t have a unified structure. Digitization of a document base solves this problem but only partially. It enables only the basic search functionality that still requires much time and is associated with the high risk of human error.
Data science along with such techniques as data mining, natural language processing, text analytics, etc. allow for extracting real insights from unstructured data. This facilitates better decision making which leads to increased profits and improved efficiency. In addition, data management solutions that are based on artificial intelligence can help bank and insurance officers search through massive stores, automatically review applications and questionnaires, generate reports, etc.
Risk management
Effective risk management is one of the most essential factors which impacts the well-being of any financial institution. In this context, machine learning is a revolutionary technology as it allows businesses to consider the whole variety of conditions which may cause different risks. For instance, this may be competitors’ behavior, market trends, news, political situation, etc. On top of that, data science helps to assess such risks and classify them based on the criterion of potential losses.
Another important data science use case in finance is software for smart underwriting decisions. Such software may accurately determine the creditworthiness of potential clients even if they don’t have a long credit history. Besides the information provided in a bank application, AI-powered underwriting solutions may review other public data related to a customer (e.g. posts on social media) and determine if such a customer may be considered trustworthy.
As a result, banks and other financial institutions have an opportunity to avoid unreliable applicants who are not going to pay off their loans. The implementation of an AI-based risk management system and a smart underwriting system also improves the company’s overall security and reputation.
Conclusion
Data is the foundation of any business activity in the financial sector. However, even the most comprehensive data has a value only if one can extract the necessary knowledge from it. Data science and artificial intelligence can help out with this task. Data-driven decision-making, personalization, fraud detection, advanced customer service, data management, and risk management are only a few examples of data science applications in finance. But, basically, you can build any custom AI-based solution that will solve your specific business problems.
