Data engineering Use Cases in financial industry.pdf

Top 5 data engineering use cases that CFOs
need to look for in 2024 and beyond

The financial industry generates vast amounts of data from various sources,
including transactions, market movements, and customer interactions. This data
holds immense potential for unlocking valuable insights and driving informed
decision making.
Data engineering plays a crucial role in harnessing this potential by building and
maintaining the infrastructure that collects, processes, and analyzes this data.
This document explores how data engineering empowers various functionalities
within the financial industry, outlining the challenges associated with each use
case, the data engineering solutions employed, and the resulting benefits. By
understanding these applications, you can gain a deeper appreciation for the
transformative role of data engineering in the financial landscape.
Gartner predicts that 90% of corporate
strategies will specifically reference
information as a critical business asset and
analytics as a critical competency.
How data engineering will reshape the
ﬁnancial industry in 2024?
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1.Risk management:
Challenges faced by financial firms:
• Data quality and consistency: Ensuring data from diverse sources is accurate,
complete, and standardized.
• Model interpretability and explainability: Understanding how risk models
arrive at their decisions for regulatory compliance and fairness.
• Model bias and fairness: Avoiding biases that can disadvantage certain groups
in loan approvals or investment opportunities.
Data engineering solutions:
• Data cleansing and transformation: Cleaning and standardizing data to
ensure consistency and eliminate errors.
• Feature engineering: Creating new data points relevant to risk assessment
based on domain knowledge.
• Data lineage tracking: Tracing the origin and transformation of data
throughout the processing pipeline for better understanding and
troubleshooting.

At JP Morgan Chase, data engineering enables the integration of market data and transaction histories, empowering the bank to perform
sophisticated risk management analysis. By combining these diverse data sources, data engineering pipelines fuel real-time dashboards
that provide efficient visibility into evolving risk profiles, allowing for timely and informed decision making.
Benefits of implementing data engineering for risk management
Improved credit
risk assessment
Reduced
financial losses
Enhanced
regulatory compliance
How JP Morgan chase used data engineering for risk management

2. Fraud detection and prevention:
Challenges faced by financial institutes:
• Evolving fraud techniques: Keeping pace with new and sophisticated methods
employed by fraudsters.
• False positives and negatives: Balancing the need to detect fraud without
generating too many false alarms or missing legitimate transactions.
• Data privacy concerns: Ensuring compliance with data privacy regulations
while collecting and utilizing data for fraud detection.
• Real-time data processing: Enabling analysis of large volumes of data in
real-time to identify suspicious activity quickly.
• Machine learning model training: Continuously training and updating models
with new data and fraud patterns to improve accuracy.
• Data anonymization and pseudonymization: Protecting sensitive customer
information while still enabling effective fraud detection.

Minimal
financial losses
Improved customer
trust and satisfaction
Enhanced
brand reputation
PayPal harnesses data engineering and machine learning algorithms to bolster its fraud detection capabilities. By analyzing user spending
patterns and establishing behavioral profiles, their data pipelines identify anomalies that could signify fraudulent activities. This proactive
approach, facilitated by robust data integration and advanced analytics models, enables PayPal to minimize fraudulent transactions
effectively, safeguarding both the company and its customers.
Benefits of implementing data engineering for fraud detection
How PayPal implemented data engineering for fraud detection

3. Customer relationship management (CRM):
Challenges faced by financial companies:
• Data silos and integration: Integrating data from various sources across the
organization to create a holistic customer view.
• Data privacy regulations: Complying with regulations like GDPR and CCPA
regarding customer data collection, storage, and usage.
• Customer churn: Identifying and addressing the root causes of customer churn
to improve retention rates.
• Data lake and data warehouse development: Centralized storage and
organization of data from various sources for easy access and analysis.
• Customer data platform (CDP) implementation: Creating a unified customer
profile by integrating data from all touchpoints.
• Data quality monitoring and improvement: Continuously monitoring and
improving data quality to ensure accurate customer insights.

Personalized
customer experience
Improved
customer engagement
Increased customer
lifetime value
As a leading multinational financial corporation, American Express employs data engineering practices to synthesize customer behavioral
data and demographic profiles. This integrated customer view, enabled by robust data pipelines, allows American Express to tailor financial
offerings, rewards programs, and loyalty initiatives to each individual customer's unique needs and preferences. By harnessing the power of
consolidated customer insights, the company can deliver highly personalized experiences that foster long-term engagement and loyalty.
Benefits of data engineering for customer relationship management
How American Express improved customer experience with data engineering

4. Algorithmic trading:
Challenges faced by financial firms:
• Market volatility and complexity: Adapting models to handle unpredictable
market conditions and complex financial instruments.
• High-frequency data processing: Managing and analyzing large volumes of
streaming market data in real-time.
• Model explainability and regulatory compliance: Ensuring models are
interpretable and meet regulatory requirements for transparency.
• Scalable data infrastructure: Building and maintaining infrastructure that can
handle the high volume and velocity of market data.
• Real-time data pipelines: Developing pipelines to ingest, process, and analyze
streaming data in real-time for immediate decision-making.
• Backtesting and model validation: Rigorously testing models on historical
data to assess performance and identify potential risks.

Faster and more
efficient execution of
trading strategies
Reduced
transaction costs
Enhanced
risk management
At GTA, data engineering plays a pivotal role in driving their quantitative trading strategies. The firm ingests massive volumes of real-time and
historical market data feeds including trades, quotes, news and social media signals across multiple asset classes. Highly optimized data
pipelines built on distributed streaming platforms process this torrential data flow. Complex event processing engines identify tradeable
patterns and opportunities, which are fed into predictive models. These models, aided by machine learning over the normalized, cleaned
data, generate trade signals that are executed by automated trading systems.
Benefits of data engineering for algorithmic trading
How Global Trade Alert is leveraging data engineering for trading

5. Regulatory compliance:
Challenges faced by financial organizations:
• Keeping up with evolving regulations: Adapting data management practices
to comply with constantly changing regulatory requirements.
• Data security and privacy: Protecting sensitive customer data from
unauthorized access or breaches.
• Data auditability and traceability: Ensuring data lineage is clear and all actions
are documented for regulatory audits.
• Data governance framework implementation: Establishing clear policies and
procedures for data management, access, and security.
• Automated data reporting: Building systems to automate the generation of
reports required by regulatory bodies.
• Secure data storage and access controls: Implementing robust security
measures to protect sensitive data and ensure access is granted only to
authorized personnel.

Reduced risk of regulatory
fines and penalties
Increased transparency
and accountability
Improved efficiency
and cost savings
Wells Fargo has implemented a comprehensive regulatory data platform enabled by robust data engineering. Orchestrated pipelines move
data from source systems into a centralized data lake, where it is cleansed, transformed, and mapped to compliance reporting templates. This
allows automated generation of reports mandated by regulations, while machine learning models identify risks proactively.
With immutable data storage and full lineage, the platform ensures Wells Fargo meets compliance obligations comprehensively while
reducing costs - made possible by treating data as strategic infrastructure through data engineering best practices.
Benefits of data engineering for algorithmic trading
How Wells Fargo ensured regulatory compliance with data engineering

Summary
Data engineering capabilities are proven to unlock business value from modern
analytics across the financial sector. From real-time fraud detection to personalized
marketing and automated regulatory compliance, advanced use cases are able to
thrive only on robust data architectures.
By leveraging scalable cloud data platforms, flexible data pipelines, and a modular,
repeatable approach to infrastructure - financial enterprises are overcoming
traditional data access challenges. The result is accelerated analytics and AI, reliable
risk insights, and compliant yet frictionless customer experiences. Treating data as a
core strategic asset for competitiveness, rather than a one-time project, underpins
success - enabling sustainable data-driven transformation.
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Data engineering Use Cases in financial industry.pdf

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