Financial institutions, both retail and commercial, have a huge amount of data on their customers.
But they still struggle to extract meaningful information and use it for reasonable business decisions. By some estimates, businesses use only 0.5 percent of available data.
In addition to this, regulatory requirements put pressure on financial institutions to control their data and provide vast levels of transparency at enormous scales.
So, how can your financial services organization overcome these data challenges in reporting and analysis?
In recent years, financial institutions worldwide have built risk-related data-control capabilities as a result of regulatory demands.
These began with the inception of the Basel Committee's BCBS 239 principles, issued in 2013, to strengthen banks' risk-related data-aggregation and reporting capabilities.
But development has not been uniform. Many organizations are still uncompliant and struggle with significant deficiencies, particularly when it comes to data architecture and technology.
The main reason for this is the addition of more principles without clear instructions on implementing them. And, combined with other regulations, such as the GDPR in Europe and CCPA in the US, this had led to a wide range of interpretations.
In a nutshell, the most recent regulations dictate that banks should:
- Build and maintain a data and IT infrastructure which fully supports risks data aggregation capabilities and risk reporting practices
- Generate accurate and reliable risk data
- Operate on a largely automated basis to minimize the possibility of errors
- Allow various levels of detail across banking group, business line, legal entity, asset type and region
- Aggregate up-to-date risk data promptly while maintaining accuracy integrity, completeness and adaptability
- Provide on-demand, ad hoc risk management reporting requests, especially during crises and adapt to internal needs
- Reconcile and validate reports
- Anticipate that the frequency of reports will increase in time of stress and crisis
This is a lot for financial institutions to work through, organize and implement. Paired with the genuine threat of some hefty regulatory penalties, the need to overcome the challenges and invest in new solutions is paramount.
Strengthen your risk-related capabilities
So, how can you overcome these issues in the least disruptive way and with tooling suited to finance?
One answer lies in the use of data models that have a unique bridge to IT operations to accelerate the adoption of stringent regulations.
The bridge provides a link between all steps from the model to production. This allows your IT department to build transparent and auditable data processes quickly. Financial organizations can then benefit from performing validation and reconciliation on data as everything lives within a single platform.
The burdens of manual processes
Regulators know the reality of working with data in banks. They know it includes manual work and slow, cobbled spreadsheets, which are prone to errors.
Historically, the link between data models and operations has been manual, unreliable and tedious. It required a cumbersome translation of models into executable runtime by developers, with each requirement taking weeks or months to implement.
All of this work, and there's still no guarantee that the model is accurate within the pipeline.
Considering the increasing demand for accurate and timely financial insights to strategic decision-making, your financial organization is ready for automation when:
- Your teams are involved with repetitive, routine and non-productive tasks.
- You spend too much time on significant manual reconciliations and the updating of multiple Excel spreadsheets for closing and reporting.
- Your current accounting system involves manual workarounds and data re-input.
- There's a frequently high error rate detected in reports.
- You have poor visualization and flat reports without flexible drill-down options.
Many organizations continue to invest significant time every day in calculating, manipulating and validating critical financial reporting data using spreadsheets.
The consequences of manual and outdated processes include:
- Thousands of sources, targets and integrations are controlled without standardization to the process.
- If people have no other option and do their own thing, you're in danger of creating siloed data. This causes a semantics of process and quality that's not helpful in reporting and analysis.
- If data sets aren't correlated, individual functions of your organization will be disparate and possibly duplicated as each department builds its solutions to its problems.
Investing in new tools that allow you to better build out your processes and data architecture means that you can shake off the status quo and focus on structure and innovation at the same time.
Build a better solution
A data model to IT operations bridge can take data, mapping and transformation definitions captured in data models and turn them into executable runtime objects.
You get the safety of having your data when and where you need it, coupled with the freedom to innovate.
A bridge that links all the steps, from the model to production, means you can ingest data into data feeds without unnecessary development delays and effort.
Employee resistance and low adoption
We can now get access to very different data types to make better decisions in almost any function. But this requires different skillsets and involves adaptation.
While it's clear staff have been pragmatic in their use of spreadsheets to find solutions to these kinds of problems, it has also led to complex environments that slow down operations significantly.
Getting around this is no mean feat.
Find your data models champion
If you give your team familiar tools that speak their language, they're more likely to adopt new models into their practices.
The CloverDX data model bridge makes it easier to champion the changes required to lead to higher productivity and shorter turnaround times. Everybody gets used to the same definitions and meanings, which in turn help to process the data models to capture and share insights.
Financial organizations can also benefit from tracking lineage and relationships, adapting to changing business needs, and creating standardization of vocabulary and definitions across departments.
In a single click conversion using the bridge, users can enjoy a guaranteed 1-1 representation of models actionable by IT.
The insight gap
Access to real-time financial data is vital for good business decisions. But traditional finance functions require a significant turnaround time to generate reports for management. Oftentimes, these end up historical-focused and unable to deliver predictive insights.
You can't get valuable insights from the data you have on client behaviour if it's scattered across the firm in separate databases.
And how can you be a strategic business partner when you spend most of your time on manual processes and issue triage?
Alongside rising new standards and the need for more detailed disclosures, complex calculations and valuations, this has created the need for a more tailored solution.
Enhance your reporting
In a data bridge to IT operations model, you can catalogue everything. You can also reuse fragments, again and again, to further save costs. Creating well-defined business algorithms for automatic testing leads to higher productivity, better insights and shorter turnaround times while increasing confidence.White Paper: How to Bridge Data Models and IT Operations for Simpler Compliance
Bridging data models to operations
Non-compliance is becoming a real (and expensive) risk for financial organizations.
And cumbersome manual processes means it can take weeks to get reports. Weeks in the middle of a crisis is too long.
That said, it's not easy to convince the C-suite to adapt to a new way of delivering predictive insights.
Over the coming years, financial organizations need to ensure they have the skills, processes, and tools in place for effective and impactful planning and forecasting.
The CloverDX Data Management Platform helps reduce development time. A data model to IT operations bridge adapts to changing business needs in a visual and interpretable way.
If you want to know more, just get in touch and we can walk you through how we've helped streamline financial services reporting for several major institutions.