How Data Science is Transforming the Financial Services Industry

Machine Learning and Artificial Intelligence are all the rage now, but their magic wouldn’t exist without the power of Data Science behind the curtains. The Data Science experts from Waverley Software are happy to provide us with some insight into the interconnection between the fields, and how the FinTech industry can benefit from the study and analysis of Big Data.

Since Data Science is all about information and figures, we suggest taking a look at some numbers, for starters. In 2016, Forrester predicted that by the year 2020, insight–driven businesses will be collectively worth $1.2 trillion. Statista’s big data statistics estimated that by 2023 the big data industry will be worth $77 billion. A study, “The Business Impact of Data Intelligent Management” held by Forrester Consulting, surveyed more than 900 analysts in 2020 to find out that data-centered organizations are 58% more likely to exceed their revenue goals.

So how exactly does Data Science work and what makes it that effective in helping businesses, particularly in finance?

What is Data Science?

Despite the fact that it’s often perceived merely as a more advanced, computerized version of statistics and is expected to provide the target users with nice and bright graphs and charts, Data Science is something more than that. Data Science is a complex field that appeared at the intersection of mathematics, statistics, computer science, information science, data analysis,  and domain knowledge. The below Venn diagram reflects the interrelation of these disciplines in framing into a new specialization. 

The aim of Data Science is to extract unobvious knowledge and value from large sets of structured and unstructured data. The use of Data Science for business lies in creating impact by finding solutions to specific problems. Depending on the company needs, the work of a Data Scientist results in informational insights, a data product, or product recommendations.

Data Science for FinTech

As estimated by Forbes, more than 150 zettabytes (150 trillion gigabytes) of data will need analysis by 2025. Out of these, the amount of data that the financial industry and the banking sector generate every second is overwhelming: just between 2016 and 2020 the numbers increased by 700%. Surprisingly, statistics say that a mere 29% of financial industry companies readily adopt AI&BI solutions.

Advantages of Data Science for Financial Industry

Despite this low adoption rate, the applications of Data Science in fintech and banking can yield particularly beneficial results:

• Enhancing security. As cyber attackers and financial crimes are still on the rise, financial organizations are only getting under more risk of exposing their customer sensitive data. Also, due to imposed regulations (such as Basel III, FRTB, MiFID II, AML/KYC, FATCA), financial institutions are obliged to disclose a good deal of information to regulatory bodies. Emerging authentication methods are another source of data to be processed. These are great prerequisites for benefitting from the data analytics and predictive patterns Data Science can bring.

• Tracking and predictions on user behavior. Financial organizations can utilize a myriad of channels for interacting with customers and collecting user data. Having the information about their customers’ browsing history, geo-location, interactions timing, interests and preferences, companies can better know and understand the needs of their existing and potential clients.
• Optimization of internal operations. A good way to reduce operational costs without losses and improve processes is to bring in analytics and automation. Data Science gives you the chance to easily make use of all the insider data you collect and store anyway, identify their value and potential through data valuation, and skyrocket your business efficiency with some awesome insights.
• Getting advantage over the competition. In the era of Big Data, this is just not enough to have and store it, it’s important to learn how to use the information you own. With wide access to shared information today, you can even collect and analyze your competitors’ data to learn lessons from.

Data Science Applications in FinTech

The Data Science use cases in the financial sector are also numerous.

First of all, finance and banking have a huge commercial component and function in the same way as other businesses do in order to reach a profit – launch sales and marketing campaigns, generate leads, communicate with customers, find their pain points, try to deliver value and so on.

More Efficient Marketing and Sales

• Target the right set of customers: with Data Science, you can leverage your collected data to discover similar spending or behavioral patterns and segment your audience.
• Identify the best channel (or a mix of channels) for your next marketing campaign, analyse direct feedback and user journeys to take lessons for building a better marketing strategy, spot influential customers and engage them as brand ambassadors, etc.
• Predict the products or services customers are most likely to be interested in by looking at your historical or test data. As a result, you will be able to choose the most effective sales strategy, boost cross-selling effort, or correctly apply dynamic pricing.

Personalized Customer Relationships

• Collect relevant data to create accurate user profiles from a ton of available personal information about users you can get from multiple interaction channels.
• Examine the customer information you have at hand to provide customized offers. Show care and match each client’s current needs. Predict further communication steps, forecast and prevent customer churn rate with logistic regression analysis method.
• Improve customer support by utilizing Data Science and automating data presentation to a support specialist while they are communicating with the customer. Don’t forget this is a great way to augment their capabilities but it cannot become a substitution for a live person.
• Use the power of Data Science and ML algorithms to automatically collect and examine customer feedback. Listen to your customers and you might arrive at valuable conclusions.

Apart from commerce-oriented activities, fintech businesses deal with large amounts of valuable information and manage enormous sums of money, so they continuously stand the risk of attracting criminals. Thus, fraud detection and protection of sensitive user data should be the top concern of any financial institution that cares for its customers’ well–being and own reputation.

Reliable Cyber Security

• Track user behavior and identify suspicious behavior patterns, then notify users and security managers of possible threats. Among such suspicious behavior patterns might be multiple accounts with similar information opened within a short term, unusually high transactions, or unusually frequent purchases of popular items.
• Use Data Science and smart security algorithms to collect and analyse data coming from your security perimeter. Amplify your cyber security with physical security measures at the facilities, and you will be able to collect even more data to process and use for security purposes.

Effective Risk Management

• Analyse the available data about the object of investment as well as other relevant information to calculate a risk score for a particular investment. Based on this score, bank consultants can draw more precise conclusions and provide data–backed advice.
• Look into the loan requester’s historical transaction data or credit history to evaluate their potential to pay back the loan. As a result, make informed and unbiased decisions as to the credit issue.

In addition to the above factors, smoothly running internal operations and processes play an integral role in the functioning of any organization. The finance and banking sector also heavily depends on the currency market, economic situation in the region and the world, various regulatory initiatives, and even political events. Data analytics is helpful in digesting this huge amount of constantly changing information and fluctuating figures.

Real–time and predictive analytics

• Real–time analytics helps financial institutions draw timely insights from the current organization’s internal state of affairs as well as ground important operational decisions on fresh external financial data: currency exchange rates, stock market news, new investments, etc.
• Predictive analytics uses historical and current internal and external data to forecast potential risks or prospects. As a result, the organization leaders have more information for building a business development strategy, polishing processes, implementing new promising approaches and eradicating old inefficient ones, for example.

Conclusion

Considering the increasing digitalization of our day-to-day interactions, it’s no wonder that every individual is becoming an active producer and consumer of huge amounts of data. Internet banking and financial applications are no exception: they make the lives of people and businesses easier by streamlining payments, money transfers, and asset management. Data Science reaches every industry domain that is intensively dealing with data and provides us with even more opportunities due to the power of computing and artificial intelligence. Grabbing this chance is no more a luxury but a need to survive and flourish in this world of data.