Constructing a Banking data tape to consolidate risk picture saving time while facilitating improved investor relations

Project Details

Title: Development of a data tape
Customer: Large German Bank
Industry: Banking
Period of project: 8-Months
Budget: 300,000 €
Est. value/ROI: > 10x - Reduced annual manpower - Precise data insight improving investor relationship management and capital structure
How many persons in project:
Used Technologies: LINUX, DB2, MS SQL Server, GitLab
Hash-Tags/Keywords: #Data tape, #Portfolio analysis, #Financial analysis
Project Category:
Risk insights
Project Tags:
Risk insights

The emergence of the digital economy over the past decade has led to a dramatic business model and industry transformation for financial services companies.

Data science and the use of AI empowers organizations to live well in the digital economy.


No standardized general data tape meant great effort and time to compile a consolidated risk picture making it problematic to present periodic risk snapshots to investors hindering investment and more difficulty maintaining investor relations. The bank did not have a standardized general data budget, but rather many different systems, some of which could not be easily reconciled directly with each other. This resulted in a very high effort to be able to draw an overall picture of the bank.


A data tape was developed that summarizes a holistic dispositive data budget from all of the bank’s source systems that analyze all key relevant decision-making criteria at the customer account level. The data tape was prepared in such a way that it connects the balance sheet and facilitates the reconciliation of balance sheet items. An agile development process was employed. execution completely in (DB2) SQL over 6 weeks with 3 data engineers and the relevant input from 15 departmental employees.


Automating the data tape resulted in a large annualized reduction of effort. The tape was constructed to allow queries of n:n relations and with no source changes only requires maintenance. Concise, accurate snapshot pictures reduced ongoing effort while making investor relationship management easier and more effective.

The system is extended by network analysis-based segmentation algorithms and serves as a development basis for a total bank control cockpit.

Ready to Progress

Data will not put companies out of business, failure to use it will.

For Banks to thrive in the data science and AI era they need to find new ways to compete and address data science front and backend resourcing gaps. Harness the power of your own regulatory compliance conforming data science platform.

Banking industry soundbites

The banking industry is undergoing a radical shift, one driven by new competition from FinTechs, changing business models, mounting regulation and compliance pressures, and disruptive technologies. The emergence of FinTech/non-bank startups is changing the competitive landscape in financial services, forcing traditional institutions to rethink the way they do business. As data breaches become prevalent and privacy concerns intensify, regulatory and compliance requirements become more restrictive as a result. And, if all of that wasn’t enough, customer demands are evolving as consumers seek round-the-clock personalized service.

These and other banking industry challenges can be resolved by the very technology that’s caused this disruption, but the transition from legacy systems to innovative solutions hasn’t always been an easy one. Global, regional, community banks and credit unions need to embrace digital transformation if they wish to overcome key challenges to survive and compete in this fast-changing data-driven digital landscape.

Increasing Competition

The accelerating threat posed by FinTechs who typically target some of the most profitable areas in financial services.

Goldman Sachs predicted that these startups would account for upwards of $4.7 trillion in annual revenue being diverted from traditional financial services companies.

Cultural Shift

Data and technology have become ingrained in our culture, and this extends to the banking industry. There’s no room for manual processes and systems. Data-enabled solutions are critical to the resolution of banking industry challenges. It’s important financial institutions develop cultural innovation, in which data-enabled decision making is leveraged to optimize existing processes and procedures for maximum efficiency.

Regulatory Compliance

Has become one of the most significant banking industry challenges as a direct result of the dramatic increase in regulatory costs relative to earnings and credit losses since the last financial crisis. From Basel’s risk-weighted capital requirements to the Dodd-Frank Act, and from the Financial Account Standards Board’s Current Expected Credit Loss (CECL) to the Allowance for Loan and Lease Losses (ALLL), there are a growing number of regulations that banks and credit unions must comply with; compliance can significantly strain resources and is often dependent on the ability to correlate data from disparate sources. A maturing data culture is critical to mitigating compliance challenges effectively. Data mining data, and analysis, provide valuable actionable insight identifying and minimizing compliance risk. Data solutions standardize processes, ensure procedures are followed correctly and consistently and enable organizations to keep up with new regulatory/industry policy changes.

Evolving Business Models

The increasing cost of capital combined with sustained low-interest rates, decreasing return on equity, and decreased proprietary trading have all placed pressure on traditional sources of banking profitability. Many institutions have been forced to create new service offerings, rationalize business lines, and seek sustainable improvements in operational efficiencies to maintain profitability.  Adapting to changing demands is essential so financial institutions must be structured for agility and resilience.

Increasing Expectations

In a digital economy, customers are more informed than ever before and expect a high degree of personalization and convenience. Shifting customer demographics impact elevated expectations. New generations of banking customers are innately aware of technology and bring an increased expectation of digitized experiences.

Client Retention

While customer experience can be hard to quantify, customer turnover is tangible with customer loyalty becoming fragile. Retention is a product of deep client relationships that begin with knowing the customer,  expectations and implementing an ongoing client-centric approach. In an Accenture Financial Services global study of nearly 33,000 banking customers spanning 18 markets, 49% of respondents indicated that customer service drives loyalty. Knowing the customer and engaging with them accordingly can optimize interactions that result in increased customer satisfaction and wallet share, and a subsequent decrease in customer turnover.

Legacy Applications

According to the 2017 Gartner CIO Survey, over 50% of financial services CIOs believe that a greater portion of the business will come through digital channels, and digital initiatives will generate more revenue and value. However, organizations using legacy outdated or siloed systems are unable to keep up. Without a cohesive data strategy and cultural appetite with a workforce of data science citizens, the competition will outperform. Data science is part of a digital transformation that is imperative for survival and to thrive.

Security Breaches

With the increasing #s of high-profile breaches, security is one of the leading banking industry challenges. Financial institutions must invest in data solutions and technology-driven security measures to keep sensitive customer data safe.

Constant Innovation

Securing the future viability of any business requires resilience. In a digital world developing systems of insight enables a cycle of reinvention through continuous innovation and capabilities to deploy at speed.

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