Banking sector
Projects from the
financial sector
We have been active in the banking sector since our foundation and support our clients in the realisation of their regulatory data requirements in long-term partnerships.
The challenges
The bank’s production line is IT. The sector therefore seems predestined for a high degree of maturity in data-supported decision-making. In reality, however, this is countered by a system landscape that has mostly evolved over time, high regulatory process requirements and the resulting high complexity of data flows and internal processes. The greatest business intelligence challenges in the traditional banking sector arise from globalised and digitalised financial markets with their emerging and highly automated neobanks and fintechs, which are changing customer needs and cost expectations and thus forcing efficient data management and decision-making mechanisms. The requirements are made more difficult by constantly new regulatory requirements such as SA-CCR, CRR3, DORA, loan origination or ESG, some of which require data that banks have not had available to them to date or only in insufficient quality.
Our contribution
Our contribution
29FORWARD has been active in the banking sector since it was founded in 2013, specialising in risk management. We understand your technical requirements and regulatory standards and can navigate your tightly meshed processes in project management as well as in development, operation and testing. Our understanding of your business model enables us to optimise support for your data management and the underlying architectures. Legacy, on premise und in der Public Cloud.
1. example project
Introduction of a new core banking system
29FORWARD took over the project management for the introduction of a new core banking system for the Czech subsidiary of an international automotive bank. This included the cyclical rollout of new financial products and the replacement of the legacy system. The new system provided the client’s financing business with a modern and future-proof foundation.
Successes
Fulfilment of regulatory requirements
Enabling new products
and faster application assessment options
2. example project
Centralisation of behavioural scoring in SAS 9.4
The aim of the project was to centralise all behavioural scoring models of domestic and foreign subsidiaries of an international financial services provider on the basis of SAS Credit Scoring. Standardised input and output interfaces and a Unicode-based data management system were introduced to connect the national subsidiaries. This provides seven years of data history in a central data model as a single point of truth for determining significant model variables in model development and annual model validation. In combination with a modelling template for generating key figure variants, this enables significantly shorter model development times and costs with consistently high model quality thanks to the specialist expertise at headquarters.
Successes
1
%
reduction in model development costs
1-2
months shorter time-to-production per model
Reduction in IT operating costs
thanks to centralised platform
Reduction in IT operating costs
thanks to centralised platform
3. example project
Risk weighted assets Calculations of various regulatory standards
The aim of the project was to automate various risk-weighted assets calculations in accordance with CRR2, CRR2 Large Exposures, Local GAAP and IFRS and IFRS9 in an international banking group on the basis of SAS Risk Stratum. Thanks to a high degree of automation, especially in CI/CD, and a high quality of technically defined use cases, which were transferred to standard tests, the time-to-production was significantly reduced while at the same time significantly increasing the software quality. This offers the subsequent processes in the month-end closing, such as COREP, a significant gain in time to be able to react to any problems in good time and thus finalise the month-end closing on time and deliver it to the regulator.
Successes
–
1
%
reduction in incident rate through automated tests
≥
1
%
