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How Does Data Science Work In Finance?

The business world is reaping the rewards of machine learning, big data, and artificial intelligence. Although the finance industry has always had an aversion towards advanced technology due to security concerns, cutting-edge data science can empower companies and steer them away from risk. Stronger fraud detection, predictive risk monitoring, anomaly detection, better sales and forecasts, and data-backed insights are only a few of the opportunities stemming from data science. 

Data Scientist was dubbed the sexiest job of the 21st century by Harvard Business Review almost a decade ago. The comedic title highlights the importance of data science for entrepreneurs and companies in finance, payments, and banking. When incorporated correctly, AI, machine, and deep learning can unlock far-reaching opportunities in almost all business aspects. 

SDK.finance, a white-label digital payment platform for financial companies, is actively developing next-generation data science solutions for payments companies so that they can leverage the substantial benefits of modern technology. 

What is data science in finance?

Data science in payments, banking, and finance is about extracting the most knowledge from vast amounts of collected data using maths and statistics. Data science can be highly relevant for risk management, risk analysis, fraud prevention, real-time anomaly detection and sales generation. With many different techniques and approaches to choose from, data science can extract valuable information from structured and unstructured sources and identify irregularities to forecast future behavior and patterns. 

Falling costs for data storage and processing, better and faster connectivity, and rapid advancement in data science in finance enable companies to improve upon human decision-making in accuracy, speed, and reliability. According to McKinsey, data science-related technologies can potentially unlock $1 trillion of incremental value for banks annually.  

Source: McKinsey

Data science in finance can boost revenues by better personalizing services for consumers, lowering costs through higher automation, and improving efficiency with better resource utilization. Financial companies that fail to rely on the improved ability to process more data faster will risk being overtaken by competition and deserted by their customers. 

According to a BCG survey, almost 90% of executives see AI as an opportunity, but only 18% have tried to apply data science to generate revenue. Even though incorporating and deploying machine learning, deep learning, and artificial intelligence into a business strategy can be a challenging process, the upside for financial companies makes it worth the effort. 

Benefits of data science in finance

Better sales and revenue

With many customers isolated and negatively affected by the pandemic, payments, banking, and financial companies need to increase interactions with their customers through high-quality, personal connections. Data science allows companies to examine their customer-facing digital experience and continuously improve it to reflect their clients’ desires. Improvements in the way data science perceives language and emotions unlock an entirely new level of customer experience personalization. 

Data science engineers can analyze consumer actions, compile models from the results, and generate behavioral insights that enable companies to offer the right services to their clients at the right time. By breaking down consumers into distinct classes and audiences based on socio-economic attributes and characteristics (age category, preferences, location), financial firms can make assumptions about how each customer is likely to behave and how much value they will generate in the future. 

This data can be used for A/B experiments learning to determine the optimal prices or fees for consumers. Adjusting the price to reflect consumer preferences maximizes income from existing and new clients alike. Similarly, ads targeted using data science are more likely to deliver better results for digital campaigns and generate better insights for marketing and sales teams. 

Source: BCG

Get helpful insights

Fraud is a significant and costly problem for financial institutions. As the number and volume of transactions continue to grow, fraud and cybercrimes will become more prevalent. On the other side, data science leverages big data and analytical software to limit finance companies’ exposure to fraud through proactive and predictive analytics. By spotting irregularities and suspicious behaviors, data-driven financial platforms can warn businesses and individuals and limit or even prevent damages outright. 

Insights generated from data-driven fraud research can be used to separate clients into even smaller cohorts. For example, trustworthy clients with a verified purchasing history and future potential can benefit from better rates or larger credit allowances as they carry less risk for the bank. For riskier clients, dynamic data pipelines can access financial data with minimal latency, allowing companies to monitor transactions and financial parameters in real-time. 

Optimize routines with Robotic Process Automation

Reconciliation and other routine operations take up hundreds of working hours for accountants and other employees. By matching transactions across multiple data sources, data science in finance can save up time and free up resources for more critical tasks. Robotic Process Automation can significantly reduce the burden of risk assessment and creditworthiness by checking all available consumer data and presenting the results in a clear manner. 

Every payment, banking, or financial institution can choose to leverage data science benefits to improve and step up its operations and routines. Extracting the full potential from available data in the form of analytics, personalization, and decision-making can meaningfully transform any financial business. 

Contact the SDK.finance team directly to talk about how data science can be useful for your payment business. We are open to discussions.

Written by Pavlo Sidelov on Apr, 08, 2021

#Data science