Empowering risk management users with data : Standardize data ingestion

Pradyumna — Fri, 16 May 2025 02:03:53 +0000

Risk management is a key function in banks to identify, access, maintain and monitor various risks. This is very important to ensure compliance with regulatory requirements and stability of the institution to continue investor confidence. Each institution will have its own structure, process to define adherence towards different types of risk functions. Most of Risk functions will involve the below steps =>
Identification: Defining and uncovering potential risks, including their root causes
Assessment and analysis: Evaluating the likelihood and potential impact of risks to prioritize mitigation efforts
Mitigation: Implementing strategies to reduce risk exposure and minimize the likelihood of incidents
Monitoring: Continuously testing, collecting metrics, and addressing emerging trends to ensure the effectiveness of controls
Reporting: Generating reports on the progress of risk management initiatives to provide a dynamic view of the bank's risk profile

Risk management is crucial now than ever given volatility in the banking industry and new risks emerging with evolution of new risks.
Most Risk functions involve judgement from the users. Key Risk management strategy would be to automate the repetitive processes as much as possible and provide them diverse data points to allow Risk users to focus on key tasks requiring judgement.
There are multiple considerations to automate a process however we will focus on the data aspect.
To automate any steps for above Risk management functions, data will be required. Moreover data is needed from different departments across the institutions.

However there are challenges to solve the data problem :-
Ability to avail data from disparate systems within and outside the institution can be quite cumbersome given data formats, data types and data storage might be different
Transform each source as per compliance needs
Lot of risk functions may require past data availability which may have been in different formats given changes in technology landscape in the last few years. For example, Data storage can be on Cloud or On premise

Lets focus on how to solve this problem of disparate data sources

Step 1=> Standardize a data ingestion layer

Approach 1=> Convert the data from different departments into a consistent format

This can be achieved by building interfaces to convert different types of source data into one consistent format

Pros :
Abstract any source changes only at interface layer where source data is read and transformed to a consistent format
Standardized format for output data

Cons :
Additional data persistence layers might be required. This will increase need to put controls for data processing
Defining common format given volume and size of the data may vary as per different needs

Below diagram shows standardization approach 1 =>

Approach 2 => Define a common interface to ingest the data for Risk management usage
Pros :
Avoids duplicated transformations for each source

Cons :
Defining common format given volume and size of the data may vary as per different needs
Building alignment from each department to publish data in requested format.

Below diagram shows standardization approach 2 =>

Summary =>
Risk management is a critical function for financial institutions
Empowering users with data would be a key strategy
Standardizing data ingestion is a key step towards enabling users with their data needs

References =>
https://www.experian.com/blogs/insights/the-importance-of-risk-management-in-banking/
https://corporatefinanceinstitute.com/resources/career-map/sell-side/risk-management/importance-of-risk-management/#:~:text=The%20Role%20of%20Risk%20Management,-Primarily%2C%20risk%20management&text=These%20potential%20risks%20can%20include,guidance%20to%20mitigate%20risks%20effectively

Enable Risk management with ML through Scalable Cloud-Native Data Management

Pradyumna — Sun, 04 May 2025 21:58:35 +0000

Financial crimes are a persistent threat to financial institutions. Financial institutions have to build intelligent Risk management systems leveraging AI/ML which can defect and present malicious activities. Ability of computing power with the evolution of cloud computing has enabled leveraging machine learning(ML) for Risk management functions like anti money laundering.

Data is the foundation of any machine learning project. One of key consideration is availability of high quality data as data quality plays a vital role in identifying and mitigating financial crimes.

Why data quality is important =>
According to Gartner, organizations in the financial sector experience an average annual loss of $15 million due to poor data quality
Data quality and integrity is a critical challenge 66% of banks struggle with data quality, gaps in important data points and some transaction flows not being captured at all

What are possible data quality challenges :-

Lack of data standardization across enterprise =>
Different systems across the enterprise may have data in various formats. For example, key data elements may have been defined using different data types across systems.
This can cause issues in merging the data if required

Accuracy and completeness =>
Data might not be complete or missing. If data is not complete then it can lead to issues in reporting which may lead to financial penalties
If data updates may not be reflected across all systems it may lead to using incorrect data for compliance purposes

Machine learning models may need data from past years which might not have been updated or may be in different formats

What are ways to solve for these challenge :
Define data management policies and enforcing the policies
Enterprises can look to define data management policies to define standards on how datasets and data elements should be defined
Technology investment
With Onset of cloud computing, building cloud native solutions which can allow for data lineage tracking, real-time validation and anomaly detection
Building an technology ecosystem to ensure adherence to data management policies throughout data life cycle

Let's look at below reference architecture :

Architecture components =>
Data Sources (Structured & Unstructured)

Financial Transactions
Customer & Credit Data
Compliance Data
External Feeds (Sanctions, Market Data, etc)

Data Ingestion Layer

AWS Glue / Apache Kafka for real-time ingestion
AWS Lambda for event-driven processing
Amazon S3 for raw data storage

Data Processing & Quality Validation

Databricks / AWS EMR for batch data processing
Apache Spark for large-scale transformations

Metadata & Data Lineage Management

AWS Glue Data Catalog

Data Storage & Warehousing

Amazon Redshift / Snowflake for structured risk data
Amazon S3 (Lakehouse Architecture)
Data Lake

Data Quality Monitoring & Alerts

AWS CloudWatch / Prometheus for monitoring
Custom dashboards using Amazon QuickSight / Power BI

Risk & Compliance Reporting

ML-powered anomaly detection for risk scoring
Self-service analytics using Databricks SQL / AWS Athena

Benefits of Cloud native solution =>
Accurate Data for Monitoring and Detection to ensure better data quality. This will enable accurate reporting for Compliance with Regulatory Requirements
Seamless capture of metadata required for data management functions
Efficient Risk Management through system controls avoid manual errors allowing key performers to focus on tasks requiring judgement

Conclusion =>
Implementing cloud native solutions will integrate data management into the data lifecycle. This will benefit internal users as required data will be available to the users in an automated manner. Internal users can focus on data analysis required to complete regulatory reporting
Improved data management will aid to reduce data quality errors which will enable machine learning/AI model development and adoption

DEV Community: Pradyumna

Empowering risk management users with data : Standardize data ingestion

Enable Risk management with ML through Scalable Cloud-Native Data Management