Financial Industries

The use of data science to enable customer solutions, risk and regulation generates potential for massive value creation. So naturally we are seeing many AI initiatives in the financial sector. For instance, improving customer experience or better risk management to increase both revenues and profitability.

But many financials are struggling with the question: how can we unleash the full benefits while we face data quality and governance issues and lack a common language? Through our combination of sector knowledge and data-science expertise we are well positioned to advise you on how to capture the value of AI.

Credit risk models are increasingly being used for business input, making it essential for organisations to (re)develop their models to align with new regulations. Updated models allow for smooth integration of the estimates within the business processes. We not only work to develop cutting edge models that fit within the regulatory framework, but also understand the business relevance. By incorporating these requirements upfront, we can efficiently help your organisation increase its return on capital and implement effective strategic risk management.

As data privacy and protection laws rapidly evolve, organisations must be aware of any changes and remediate their data as necessary. Data remediation consists of several steps, the first focus point is to understand the current state of the data and its intended use within the organisation, after which a remediation strategy will be implemented. We offer an end-to-end solution to help your organisation with cleansing, migrating and organising its data. Managing the quality and trustworthiness of the data ensures that it is fit-for-use.

Having your organisation’s models validated is necessary to ensure their accuracy and improve the data quality and quantity. An end-to-end model validation approach allows us to not only produce a full validation report, but also include stakeholder management with the model’s users and developers. Our process is personalised, allowing you to select the depth and breadth of the validation scope to fit the purpose of your organisation’s validation assignment.

Transaction monitoring models often produce many false positives; namely, legitimate transactions that are defined as suspicious or risky through the monitoring process. This leads to both wasted time and resources to investigate these false positives while simultaneously distracting from legitimate ones. Our end-to-end approach will help your organisation reduce false positives and increase operational efficiency by assessing its transaction monitoring framework, developing customer risk profiles, and implementing improvements in the Systemic Integrity Risk Assessment (SIRA).

Datasets are becoming increasingly large and complex. At the same time, data is quickly becoming an indispensable asset making the task of generating insights integral but also more time-consuming. A management dashboard bridges this gap and enables management to oversee their data in a comprehensible and centralised manner. By optimising your data-infrastructure we are able to tailor a dashboard to your organisation’s specific needs. We will help you gain clear insights that help meet your targets and measure progress against your most strategic objectives through robust and interactive visualisations.