7 ways how data science fuels the FinTech revolution
Michael Lewis’ bestseller “Flash Boys” has brought the issues of high frequency trading into the realm of public discussion. The book illustrates in a thrilling manner how entire investment banks are ruled by the power of algorithms and data science, spotting trading patterns and windows of opportunity in real-time fashion. However, in a fiercely competitive environment like trading, data science approaches have already matured. Today, even physical details such as the location of servers in relation to the geographic location of the stock market could have an impact on the success of specific trades. In other areas of the financial sector, however, data science still empowers a revolution. Here are 7 ways how data science is at the core of the current transformation of the financial sector.
1. Payment and Transactions
Analysis and prediction of transaction volumes is key to enhance product value for customers. Data science enables better classification of payment records and thus allows banks to tailor additional services to their client’s needs. This may vary from simple analytical features (How much did you spend on groceries last month?) to more advanced features such as the integration of payment records and personal data to allow for recommendation, loyalty rewards and other forms of proactive engagement. Generally, data science facilitates the holistic analysis of customer behaviour across all channels of engagement.
2. Credit Risk Evaluation
With the vision to make “credit accessible for more people”, various FinTech startups are on the rush for clients and VC money. Their value proposition boils down to a “faster and more accurate credit risk evaluation” process than at traditional banks, which enables them to reach a broader client base and minimize credit default rates. Reliably assessing the creditworthiness of an individual not only requires the consideration of various data sources (some start-ups claim to include more than 15.000 data points, as obscure as “how fast does one type when filling out the credit application online”) in a robust model, but also the calibration against training data (historical credit data etc.). The more predictive power one can accumulate, the better the business case.
3. Revenue and Debt Collection
Data science enables the utilization of powerful predictive models in order to optimize revenue and debt collection. Already at the moment of purchase it is possible to predict a probability of timely payment, thus making revenue collection more transparent. At the same time, insights of behavioral economics and predictive modeling can be applied to allow for a more successful debt collection, once due dates have been surpassed. Determining the optimal strategy to approach debtors is a delicate undertaking, which should not be left to random guessing. Recently launched start-ups such as Pair and Collect.ai underline the potential in this field.
4. Customer Journey Attribution
Customer Acquisition Costs and Customer Lifetime-Value are – as in most business models – key metrics for banks and financial service providers. Thus, minimizing churn rates and optimizing conversion rates are crucial activities within most financial organizations. Data science enables the structured understanding of all kinds of interaction data – from unstructured text, social network activity to direct feedback rankings – throughout the entire customer journey. Thus, enabling the efficient spotting of customers, which are likely to quit the service or identify those, which could be targeted for upselling activities.
5. Fraud Detection and Prevention
While they have been on the agenda even before data science became a recognized term, fraud detection and prevention are still among the top priorities of FinTech executives. Especially with the rise of e-commerce, fraud detection and early warning systems have evolved rapidly, all powered by data science, which allows for the real-time assessment of fraudulent motives on any given payment.
6. Portfolio Optimization and Asset Management
While portfolio optimization and asset management are generally among the more mature fields of application of data science, there are some exciting new avenues. Impact analyses and correlation with asset price developments are now executed in a seamless manner and allow for more creativity and insight. Testing and optimization of capital allocation strategies can be fully automated, which makes cuts on overhead costs feasible.
7. Corporate Compliance & Service Quality
Certainly not on the innovation end, but for larger institutions still of vital importance: implementation and tracking mechanisms for compliant behaviour across the entire organization. Designing processes and architectures, which allow for early warnings and flagging in case of non-compliant behaviour is crucial for companies listed on the stock market. The issue of corporate compliance is also highly entangled with service quality, as it all drills down to one essential: utilizing all accessible data, across all levels of the organization to not only ensure legal compliance and excellency, but also spot potential for improvement for activities along the value chain. Data science empower those engines for maximum impact.