Our financial services heritage lies at the core of our offering. Our team has extensive senior level experience across trading, structuring, risk management and finance. We combine this financial services experience with cutting edge data analytics technologies to provide value to our clients
The last few years has seen fundamental change in financial services. Regulatory bodies across the globe have sought to enhance the stability of the financial services sector; increasing the demand for greater control, compliance and reporting infrastructure.
Increased competitive pressure has driven the demand to improve processes, reduce costs and optimise capital efficiency.
At the heart of all of these challenges has been the need for our clients to make better use of their data; to enhance controls and improve reporting to empower senior management to improve competitive performance.
Our deep financial services experience and data analytics expertise means we can help clients navigate this ever evolving landscape and deliver value.
Our approach has been built upon pragmatic delivery: hands-on consultants with direct, real banking experience leveraging powerful data analytics tooling to deliver real business value.
This hybrid set of skills has enabled us to help our clients answers the challenges posed by increased levels of regulation, and to rapidly deliver targeted solutions to ensure regulatory compliance.
Our data analytics tools enable us to source highly granular data across our clients’ IT landscape. We cleanse and normalise this data and then develop the business logic to rapidly deliver solutions via a repeatable process.
Granular data provides evidence based analysis that enables stakeholders to make better decisions: especially when displayed in the latest data visualisation tools to allow information to be consumed in a clear and transparent form.
As both users and developers of quantitative models and structures, we bring a high level of technical knowledge to address our clients’ needs, which are ever more demanding and sophisticated.
We have extensive experience supporting the delivery and validation of complex derivatives pricing and risk models including regulatory models, such as CRM and EPE.
Behavioural pattern analysis: allowing internal control teams to isolate and track trader patterns to identify potential anomalies has been an example of “Big Data” techniques having a practical useful outcome in financial services.
Valuation and Reporting
Spreadsheets, spreadsheets, spreadsheets. Finance runs on them, nothing beats them for flexibility. They are often used as the “temporary” stepping stone to move data between systems and to generate the multiple flavours of the same data for different end users.
But it’s not robust and it takes time.
However, modern tools now exist that can give the flexibility and dynamism of spreadsheets combined with robustness and data control to allow automated, secure runs.
These automated runs save time, remove the need for manual intervention and eliminate manual errors whether in the finance, customer reporting or regulatory reporting space.
The scope of regulatory capital and compliance reporting just keeps growing and as capital becomes ever more precious, correct regulatory calculation treatment of assets is vital.
CPRA’s unique combination of banking and data analytics expertise has led to successful delivery of our clients’ regulatory projects, from calculation to visualisation to automation.
Correct Capital Treatment
Though the focus is often on the calculation engine, improved management and treatment of input data can have a significant impact on capital efficiency.
Poor data quality and transformation processes often result in derivatives positions being excluded from Expected Positive Exposure (“EPE”) simulations and defaulting to the current exposure method (“CEM”).
Whilst BCBS 239 requires firms to source risk data attributes from disparate sources, normalise this data and track lineage to allow it to be aggregated and reported to regulators.
There is no getting away from reconciliations: they happen whenever two related data sets are joined. But keeping up with the layers of business rules, exceptions, input variations and formats makes this hard.
Reconciliations are often in two forms i) transactional (numbers and rules) and ii) text-based such as inventory and client names. Each have their own challenges.
CPRA’s data analytical tools allow it to focus on the data analysis, rather than the technical aspects of handling the data, creating a living picture of the data flow: which is precisely what a reconciliation needs.