Take a closer look at the financial services industry today, and you’ll see that it looked completely different 5 or 10 years ago. Due to the disruption caused by fintech startups, traditional banking and financial institutions had to embrace technology solutions – and most of the time, these solutions made use of data.
Data is easily the most valuable resource for finance companies today. It allows them to learn more about customers to predict their future needs, identify fraud before it causes any damages, and automate their processes to achieve operational efficiency.
But how exactly is data science applied in finance? Which solutions in the finance industry today make use of data science? Keep on reading this article to learn how data science is applied in finance and get inspired to develop a digital solution for your company.
Table of contents:
- What is data science?
- Applications of the data science in finance
- Finance and data science – all the benefits
What is data science?
Every year, the financial services industry generates hundreds – if not thousands – of petabytes of data. Every single transaction leads to the creation of new data. The sector also embraced technologies like the Internet of Things, and these devices generate even more data.
The types of financial data are now more varied than ever. Apart from business intelligence tools, finance companies now need to log and analyze text-based data such as social media comments or reviews.
Finding a way to build a data strategy across these dimensions is more challenging than ever. But unlocking the value from data leads to incredible success, as the fintech startups turned into unicorns illustrate.
Since the data problem is both difficult and potentially valuable, a special profession for dealing with data emerged – it’s called data engineering. Data engineering is all about handling multiple sources of data and transforming data so that other specialists – for example, data scientists and machine learning experts – can use it.
And finally, we arrive at data science itself.
Data science is a field that aims to extract meaning from complex and large-scale data sets. Data scientists allow businesses to focus on the insights that are valuable – for making accurate forecasts, minimizing risk factors related to financial decisions, and understanding customers better.
Applications of data science in finance
1. Risk analytics
Risk is a common component of running a business, but it’s especially prominent in the financial sector. Understanding the risk factor before making a decision is key – and this is where risk analytics comes in.
Using modern data science tools, banks can protect themselves against security threats. This is especially true if a lot of the organization’s risk is related to data that is unstructured. Analyzing it without the help of data science technologies is challenging and potentially subject to human error.
Moreover, data collected over time can provide incredible value to banks. Historical data offers insights into where losses were made and helps employees understand why that happened. The significance of a given loss and the frequency of its repetition can highlight problem areas that carry the biggest risk. That way, banks can avoid similar situations in the future. Once they identify a threat, they can rank it in line with their priority scheme and monitor its occurrence to take action whenever necessary.
Naturally, financial institutions are also in the business of analyzing the reliability of their customers. Machine learning algorithms are used here to browse through past transactions and general customer information to evaluate the relative risk of taking on a given customer or lending money to them.
Search models can instantly react to any new transactions or changes in customer information. Companies can develop a credit score for individuals based on what data scientists taught the algorithm about the specific type of customers and examples of historical risk.
2. Real-time data analytics
Until quite recently, finance companies didn’t have access to real-time data. Essentially, all the analyzed data would already be out of date. Such data were processed in batches which meant that until a given update was completed, no analysis could take place. But drawing any insights from this data didn’t make sense. Finding meaning in figures that are days, weeks, months, or even years old is a bad idea.
Today, banks can use real-time data analytics solutions that bring data science, machine learning, and business intelligence together to deliver the best experience using data that changes in real time.
Businesses can respond to customer interactions instantly thanks to dynamic data pipelines, data streams, as well as a faster relay of data between the source and the analyst. As a result, real-time analytics helps to minimize the delay in identifying the value of customer interaction. Constantly updated credit scores and transactions are far more accurate and provide a better basis for decision-making.
3. Managing customer data
Another application of data science in finance is about effective customer data management. Until recently, financial organizations processed customer data using machine learning capabilities in their business intelligence solutions. The rise of big data – and especially big volumes of unstructured data – made this method less effective.
Since there are more interactions taking place now than ever, there is even more valuable data that algorithms could be analyzing. And a lot of this data isn’t structured – coming from the Internet of Things devices or social media. Thankfully, data science comes to the rescue here thanks to the tools like data mining, natural language processing, and text analytics.
Such solutions lead to a significant upgrade in an organization’s analytical capabilities and generate deeper insights into customer behaviors in market trends.
4. Consumer analytics
Banks are now becoming customer-centric because they’re in tough competition with other finance companies and innovative fintech startups that make easy-of-access and customer-centricity their primary goals.
To provide personalized service to customers, a company needs to be able to process customer data quickly and efficiently. Real-time analytics work together with historical data trends to immediately analyze each customer’s transaction history and achieve goals such as:
- measuring the lifetime value of a consumer,
- increasing up-selling or cross-sales opportunities,
- eliminating below zero customers.
5. Personalized services for customers
Banks and insurance companies use data science to maintain a certain level of security while also providing service to customers on a case-by-case basis.
Personalization is one of the strongest trends in the finance industry today, and data is its essential component. Becoming customer-centric means personalizing the experience for each individual customer. And it’s not just about the risk assessment. It’s about building a reputation and boosting the customer lifetime value.
Everything – from phone conversations to customer reviews – can be analyzed to offer value to both the business and the customer. Understanding these interactions helps to create opportunities for valuable cross-sales by providing customers with products that match their exact requirements.
Software based on speech recognition or natural language processing can analyze these interactions and highlight opportunities to maximize sales, improve customer experience, and even lead teams in developing their future plans.
6. Customer service
Together with personalized services comes the ability of data science to launch truly advanced customer service. Since the analysis of customer interactions can now happen in real time, customer service representatives can receive better recommendations from the system and handle the customer case as the conversation is happening.
For example, natural language processing solutions can show opportunities for giving actionable financial advice based on what the customers are saying. Digital solutions that analyze the customers purchasing patterns or pass transactions can suggest the next best actions to take to make a maximum impact during a sales interaction.
All of this enables the representative to cross- or up-sell effectively, all the while addressing the customer’s query. Data from every interaction can then be used as a foundation for similar interactions in the future. This means that the system is only going to become more and more effective with time.
7. Predictive analytics
Predictive analytics solutions allow businesses to extrapolate from current data and predict what will happen in the future. This includes changing trends, customer behaviors, or risk.
In the world of financial services, the ability to tell the future has massive consequences. Machine learning applications can learn from the past stock pricing and predict the stock market prices based on that to help customers make investment decisions or build smarter trading strategies.
This is where machine learning algorithms come into play – engineers feed them pre-processed data so that the algorithms learn how to predict future events. The more data, the better the predictive model.
Moreover, the deep learning method can transform data on its own, without any pre-processing required from human data engineers.
8. Fraud detection
The increase in financial transactions means more potential occasions for fraud. Identity theft or credit card schemes are now commonplace, so it’s essential that banks minimize the damage they cause by identifying issues as quickly as possible.
That’s why finance companies turn to data science tools that identify fraud early on. AI and machine learning algorithms can detect instances of credit card fraud far more accurately than other systems because they derive trends from a large quantity of data. They’re also able to react to suspicious activity in real time.
For example, if a large spend suddenly appears on a credit card owned by a customer who historically has behaved quite conservatively, the card can be canceled, and a notification sent out to the card owner. This protects the customer, bank, and the customer’s insurance company.
And in stock trading, machine learning solutions easily identify anomalies and instantly alert financial institutions to launch a rapid investigation.
Finance and data science - all the benefits
There’s no denying that data science plays a critical role in the financial services industry by solving its core problems that arise from new customer behaviors and technology trends on the market.
Advanced algorithms, real-time data analytics, anomaly identification are just some of the many ways in which data science is disrupting the sector. If your company operates in the financial sector, it can use data for:
- smarter risk management,
- improved profits and smaller losses,
- automated and faster fraud detection,
- personalization solutions to build a truly customer-centric business.
Are you looking for an expert team to help you implement any of the examples we shared above? Don't hesitate to get in touch with us. We will help you understand the potential value of data science for your business.