When you think of data engineering, what comes to mind? For some, it may conjure images of a stereotypical IT department solving office business issues. Others may think of programmers closeted away, building an immense and complex computer model. In reality, though, if you use data (read: any information), you are most likely practicing some form of data engineering every single day.
Classically, data engineering is any process involving the design and execution of systems whose primary purpose is collecting and preparing raw data for user consumption. Said differently, any tools or steps we use to help us utilize data can be considered data engineering. In practice, having a well-designed process or system facilitates effective reporting and enables confidence in critical decision-making.
Specifically within financial services, data engineering plays a pivotal role in optimizing risk management policies, redefining investment strategies, deriving market insights, and monitoring and maintaining compliance factors. Data engineering serves as a fundamental support enabling financial services to unlock the full potential of their information. This ensures informed and strategic decision-making in an era where data-powered insights bestow a competitive edge.
In this blog, we will explore the most commonly applied use cases of data engineering in the financial industry. We will also include a sample reference architecture for a modern data stack.
Use Cases of Data Engineering in Financial Services
Use case: Data Engineering assists in gathering and synchronizing financial data of different forms into a centralized data repository. These various forms include (but are not limited to) financial data, transaction logs, credit scores, and other economic metrics. By utilizing data engineering best practices, a unified approach to comprehensive risk modeling and evaluation ensures consistency in the evaluation and comparison of data.
Benefit: Data Engineering streamlines data integration and normalization, providing a precise and accurate risk assessment. The application of data engineering can also enable the ability to foresee potential hazards and pre-determine market movements.
Example Use Case: JP Morgan Chase applies the concepts of data engineering that help combine market data and transaction histories, making it feasible for banks to carry out ideal risk management analysis with efficient dashboards created from real-time data.
Fraud Detection and Prevention
Use case: An effective machine learning algorithm for fraud detection requires significant real-time data in order to be accurate and raise fewer false positives. By analyzing spending behavior and patterns, algorithms will be able to detect anomalies and block fraudulent transactions before they happen. Data engineering creates a single source of truth on which the fraud detection algorithms can be trained to find outliers and anomalies, which help minimize the chances of fraud occurring. For a fraud detection algorithm to work efficiently, it needs historical and real-time monitoring of patterns to point out unusual activities pre-emptively.
Benefit: Data engineering helps detect unusual anomalies in vast volumes of data financial companies process. Using this capability, data engineering safeguards the interests of companies and their customers.
Example Use Case: PayPal has implemented data engineering and machine learning algorithms that help in the efficient detection of anomalies and also keep frauds from happening. They analyze user spending behavior and establish set patterns to keep fraudulent activities to a minimum.
Customer Relationship Management or CRM
Use case: Customer data is very personal and sensitive in nature. Exposure to sensitive personal data can lead to identity theft and other loss of revenue issues. When companies are able to utilize this data responsibly, though, they can introduce customer-centric and personalized services. Data engineering helps achieve this by enabling the integration of all customer data available across various sources, including transaction histories, chosen preferences, and more. This data is stored in a centralized system that protects sensitive details, providing an ‘eagle eye’ into customer patterns and behavior.
Benefit: Financial institutions carefully analyze customer data and segregate it to create a detailed customer profile. Using data engineering, institutions ensure a competitive edge by creating personalized offerings tailored to their customers’ needs.
Example Use Case: American Express, one of the most well-known multinational financial corporations, leverages data engineering practices to blend customer behavior and customer data. This facilitates generating custom financial offers, exciting rewards, loyalty programs, and more specific to each customer’s needs.
Challenges of Data Engineering in Financial Services
Given the significant opportunities available through the application of data engineering to financial services, one might wonder why more businesses aren’t adopting and emphasizing these practices. Some of the barriers preventing partial or wholesale implementation of advanced standardized processes are challenges rooted deep within a large number of organizations:
Outdated systems — Unlike other industries, the financial services industry cannot quickly adapt to newer and evolutionary technology trends. Several companies within the financial industry still rely on IBM’s old technologies for their operations. This invites complications while handling a larger amount of data than necessary (over-use of computing resources). Thus, outdated systems cannot sustain the competition that newer and modern systems provide regarding processing speed, handling, and storing of data.
Security risks — Financial institutes that operate with outdated systems are vulnerable to data theft and other anomalies. This vulnerability increases proportionally as the amount of data involved scales to greater levels. Institutions utilizing outdated systems to store significant amounts of data need to adopt measures that can strengthen their security against data breaches and fraudulent access. Cybersecurity continues to be a concern for several of these institutes.
Opportunities of Data Engineering in Financial Services
Financial services leveraging data strategy and data engineering have direct access to several key opportunities, including the following:
Cost efficiency: Since data engineering streamlines several processes, it reduces the time taken to complete individual tasks, significantly reducing operational costs.
Personalization: Data engineering paves the path for customer data analysis, enabling financial services to enhance customer experience through personalized offers, loyalty programs, and more.
Use of advanced analytical techniques: Data engineering allows for the successful implementation of machine learning and other advanced technologies. Efficiently utilizing this data allows companies to observe and analyze trends, behaviors, and patterns, ultimately drawing insights and logical conclusions that benefit the business.
Real-time decision making: Data engineering grants the capability for financial institutions to respond to data in real-time, thus making the response time quicker than ever. When it comes to fraud detection and risk assessment, every moment counts, and being able to leverage mass amounts of data in real time is a true differentiator.
Sample Architecture Solution by phData
Problem: A Fortune 500 financial institution still relied on its on-prem data lake to inform business decisions—this often led to delays in customer communication and inefficient use of legacy systems and human resources. The client needed a partner to set up a cloud data platform and operationalize the new reporting, alerting, and QA environment.
phData’s Solution: After an initial training engagement, the client brought phData in to handle both its move to Snowflake and the setup for supporting data workflows. Our data engineering team quickly got the client up and running, setting them up for automation as they continue to build out their presence in the cloud.
While data engineering may only sometimes be immediately associated with financial services, several key advantages and business opportunities are available through a standardized engineering approach to information management. Large financial companies such as JP Morgan Chase, PayPal, and American Express benefit from data engineering practices and help set possible examples. Fraud detection, risk management, and Customer Relationship Management (CRM) are some areas that significantly benefit from data engineering.
Specific challenges still need to be addressed concerning security, compliance, and usage of outdated systems. These issues continue to threaten financial services and related industries, but surpassing these barriers (which can be overcome) and implementing data engineering practices enables a new world of possibility. Efficient data processing opens the door to significant opportunities, including (but not limited to) the application of Machine Learning, the ability to make decisions in real-time, the reduction of operational costs, and the personalization of products.
We at phData have a team of experts with multiple case studies and numerous years of experience in the Financial domain. Feel free to contact us to upscale your financial services, make your organizational services more efficient, and streamline the process.
You can use Data Engineering in financial services for multiple use cases like risk management, fraud detection, price optimization, customer analytics, and many more.
The financial data needs to be stored securely and also needs to be compliant with the data laws according to the local region. Outdated systems also pose a major risk and are susceptible to hackers.