Case study

Customer 360 for Retail Banking. From Concept to Implementation:

Agile based design and development of Credit Risk reporting and Customer 360 capabilities on Cloud. This case study describes a ground up approach in building data marts for credit risk reporting at a global scale and adoption of advanced analytics with Customer 360 insights.  A large Fortune 50 Bank was challenged with integration of data silos, comprehensive reporting on credit risk across 100+ countries and pressing needs to mitigate risks through decision based analytics. At the heart of the problem was identification and servicing the customer within SLA. Our experts were requested to consult with the company’s senior stakeholders, design and implement reports, analytics and Customer 360 insights.
Who should
‍benefit from this?
Individuals interested in the following areas:
End Customers
Risk Officers
Lending Agencies
Customer Service Agents
Sales and Marketing
Financial Analysts
Business Stakeholders
Compliance and Regulators
End Customers
Risk Officers
Lending Agencies
Customer Service Agents
Sales and Marketing
Financial Analysts
Business Stakeholders
Compliance and Regulators
The Problem
Credit Risk is a complex process that needs a lot of calculations and decision points. The Retail Bank wanted to create Data warehousing Platforms for Credit Risk Reporting and Analytics across it’s countries of operations, that would guide Risk Officers, Lenders and Compliance to look at various Risk Metrics (KRI) and KPI’s as well as measure Cycle Times for Risk Lending, Loan Defaults- Probability of Defaults using Debt to Income Ratio, Credit Scores, Loss Given Defaults, Customer Loyalty, Risk Concentration and Coverage Ratio.

The Bank also wanted to advance its offerings through Customer 360 Insights that could visualize the client portfolio holding, asset class, risk measures and default history.
The Solution
Our consulting and advisory members engaged the client with preliminary discussions on requirements and needs analysis. Due to the large distribution of data assets in North America and overseas, it became imperative to construct a Business Data Glossary and Common Data Dictionary (CDE) for each area of operation.

There were several Architecture and Design components that encompassed Datawarehouse paradigms, semantic models for reports, dashboard design and concept frameworks for customer 360 insights. The Architecture, Data Models and Reports in Business Objects and Cognos- were designed as cookie cutters. This allowed for modular development with a reusable framework. The objective was to minimize customizations and reduce turnarounds country by country.

The delivery team was comprised of Business Sponsors, Product Owners from Business, Project Managers, individual Business Data Stewards, Data Analysts, Architects, Developers, Testers, Infrastructure teams, Data Privacy and Compliance. Worked with multiple vendors of choice in each region plus the core matrix team that deployed the solutions across regions.
Expected Outcomes
Anshar Labs operates on a Concept to Implementation Model. Depending on where our customers are in their journey- we establish ideations and implementation plans accordingly. We certainly believe that One Size Does Not Fit All. We strongly believe in putting ourselves on our customer’s side.

Some of the deliverables to the Bank were Business Data Glossary, Data Profiling, Data Quality, Solution Architecture, Reference Data Architecture, Data Models, Datawarehouse Platforms, Data Stewardship UI, Customer 360 Insights and Advanced Analytics.
Our Project Delivery Framework
1. Discovery and Consultative    Exploration
Our consultants brought a unique blend of design thinking and engineering acumen to the client. This comprised of a comprehensive analysis of the client's business objectives, data portfolio, and analytics needs. In collaboration with the client, we delved into understanding their challenges, target demographics, critical performance metrics, and data ecosystem.
Example- We had several sessions with business stakeholders in each region on Ideations and Needs Analysis. We explored the business data, established business data glossary, ran data profile statistics and offered data quality enhancements.
2. Architectures and Data     Models
We worked closely with data stakeholders, regional enterprise architects and data stewards in implementing Solution Architectures, Reference Data architectures and System Integration Architectures towards Data warehousing, Data Marts, Customer 360 Platforms and Dashboards.
Example- The Architectures were designed for reuse across regions of operations and the data models followed paradigms for enterprise data warehouse and data marts such as ER Models, Star Schema and Open Frameworks.
3. Data Ingestion and     Distribution
Data Integration for Datawarehouse and Customer 360 were enabled with Batch as well as Real Time data loads. Data was streamed from multiple sources such as Credit Risk Data Stores, Lending Systems, Peoplesoft etc.
We provided an Any-to-Any Framework design for Datawarehouse and Customer 360.
Example- Talend was used as an open source integration tool along with multiple databases in Oracle, Sybase, DB2 and Custom SQL. The semantic models were built on SAP Business Objects and Cognos Cubes.
4. Cross Functional     Collaboration:
We established federated development teams across regions governed by a core matrix cross functional team. There were Business Stewards and Product Owners from individual business sponsors in each region. Conducted Daily standup calls and weekly stakeholder meetings using tools like MS Project and JIRA for tracking. Issued Monthly status report to Directors and MD.
Our Project Delivery Framework
1. Discovery and Consultative Exploration
Our consultants brought a unique blend of design thinking and engineering acumen to the client. This comprised of a comprehensive analysis of the client's business objectives, data portfolio, and analytics needs. In collaboration with the client, we delved into understanding their challenges, target demographics, critical performance metrics, and data ecosystem.
Example- We had several sessions with business stakeholders in each region on Ideations and Needs Analysis. We explored the business data, established business data glossary, ran data profile statistics and offered data quality enhancements.
2. Architectures and Data Models
We worked closely with data stakeholders, regional enterprise architects and data stewards in implementing Solution Architectures, Reference Data architectures and System Integration Architectures towards Data warehousing, Data Marts, Customer 360 Platforms and Dashboards.
Example- The Architectures were designed for reuse across regions of operations and the data models followed paradigms for enterprise data warehouse and data marts such as ER Models, Star Schema and Open Frameworks.
3. Data Ingestion and Distribution
Data Integration for Datawarehouse and Customer 360 were enabled with Batch as well as Real Time data loads. Data was streamed from multiple sources such as Credit Risk Data Stores, Lending Systems, Peoplesoft etc.
We provided an Any-to-Any Framework design for Datawarehouse and Customer 360.
Example- Talend was used as an open source integration tool along with multiple databases in Oracle, Sybase, DB2 and Custom SQL. The semantic models were built on SAP Business Objects and Cognos Cubes.
4. Cross Functional Collaboration:
We established federated development teams across regions governed by a core matrix cross functional team. There were Business Stewards and Product Owners from individual business sponsors in each region. Conducted Daily standup calls and weekly stakeholder meetings using tools like MS Project and JIRA for tracking. Issued Monthly status report to Directors and MD.
The Technical Approach
At the foundation of our approach were Common Business Definitions and Glossary (CDE), Onboarding and registry of key enterprise business data elements, their definitions, technical metadata, lineage and ownership details that defined robust Data Governance.

We Architected, designed and implemented Data Models for Data Warehouse Platforms and Customer360. We ran Data Discoveries through Data Profiling and re-usable Data Quality rules for Customer, Location, Product and Transactions across business lines and regions.

Our Any-to-Any Unified Data Framework for Customer 360 offered Integrations using Batch, Near-Real Time and Real Time APIs with Internal and 3rd Party Vendors.

We Architected, designed and implemented Data Models in Erwin for Credit Risk Data Warehouse and Reporting Platforms. ETL tool used was Talend and SAP Business Objects reports. Implemented several layers of data abstractions using Oracle, DB2 and Sybase Stored Procedures, Materialized Views and DB Objects.
Benefits
In over a year the Bank was able to expand to 600 credit risk reports across globe with reusable data warehouse framework and report templates. Onboarded 200M+ Client details across the entire Bank’s client portfolio.
Key benefits realized by our client encompass:
1. Effective Data Governance and Enterprise Data Management: Our offerings ensured highly efficient    and advanced data first culture with implementations of Data Warehouse Framework, Business    Data Glossary and Data Quality.
2. Efficient Customer Service: Significant improvements in SLA with real time Customer Identification,      Risk Assessment, Registration and Rendering Services.
3. Enhanced Marketing Efficacy: Strategic advantages of enabling Customer 360 for Risk     assessments, KRIs, Customer Segmentation, Journey Maps and Risk Analytics.
4. Sustained competitive advantage: The bespoke nature of customer 360 platform furnished     business with exclusive customer insights, establishing a durable competitive edge.
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Conclusion
With effective Data Governance, Data Quality and Data Warehouse Platforms at the Retail Bank, 200M+ clients were onboarded to assess credit risk through reporting, KPIs and customer 360 insights. We supported our client every step of the way in standing high efficient Data hubs across North America, Asia, Europe and South America.
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