Top Use Cases of Data Engineering in Financial Services
Information, be it intricate calculations and meticulous record-keeping to astute market observations, has always been the foundation of the financial services market. However, in today's age, where more and more things seem to be driven by data, the volume of information has understandably skyrocketed. To no one's surprise, this change has had massive implications for decision-making in the context of financial services. You see, efficiently dealing with all this data and information and extracting meaningful insights would seem insurmountable, at least without advanced tools and techniques. This is precisely where data engineering steps in on the scene.
If you also wish to learn about the effect of data engineering on the financial services world, you are in luck. In this blog, I will walk you through what data engineering is about and afterward I will examine the different use cases of data engineering in financial services.
What Is Data Engineering?
Data engineering is the creation of system(s) that are designed to collect, store, and transform raw data into a pre-decided format. This format is, of course, conducive to analysis and decision-making. It serves as the vital link between raw data and actionable insights.
Use Cases of Data Engineering In Financial Services
- Fraud detection: It is no secret that financial services providers must scrutinize millions upon millions of transactions every single day as part of the constant efforts to find and thwart potentially fraudulent activities. Data engineering solutions, then, have come to play a critical role in this regard, helping financial services providers put together pipelines that are able to aggregate data from various sources such as transactions and social media. This process enables the development of real-time fraud detection models driven by machine learning techniques. Thus, this helps effectively identify and prevent fraudulent transactions before they cause harm.
- Risk management: Even the folks who are vaguely familiar with the world of financial services would know that risk management in this industry is quite a Herculean task. The process involves appraising loan applications, analyzing creditworthiness, and forecasting potential financial liabilities. Data engineering understandably assumes an important role here as well: it helps by establishing data lakes that bring together customer, financial, and market data. As a result, intricate risk models are made using diverse data metrics -- all of this underpins the efforts to facilitate informed decisions regarding loans, investments, and strategies for mitigating risks.
- Customer relationship management: For Customer Relationship Management (CRM) within financial services, the goal is to understand customer behavior, forecast churn, and adapt personalized financial offerings. Data engineering helps by tidying up and pooling customer data obtained from different touchpoints such as online banking, mobile apps, and branches. From there, this process becomes about facilitating advanced analytics for customer segmentation, anticipating churn risks and delivering customized recommendations and offers.
- Compliance: One must understand that when it comes to compliance within financial services, the aim is always to ensure that the company adheres to the stringent regulations. This is only possible by accurately monitoring and reporting financial activities. What does data engineering do here? Well, it helps by setting up secure data management systems that align with regulations such as Know Your Customer (KYC) and Anti-Money Laundering (AML). This initiative leads to automating data reporting processes, streamlining audits, and ultimately minimizing compliance risks.
Final Words
These are, of course, merely a handful of examples. The significance of data engineering extends across multiple domains, such as streamlining operations, market evaluation, product innovation, etc. Adept handling and scrutinizing data is the key for companies to secure a competitive advantage.
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