Data Engineering

Data is, or should be, the cornerstone of most financial institutions. It can be used to implement effective fraud prevention measures, identify customer behaviours and reshape product offers accordingly, as well as make important metrics more accessible across organisations. 

Yet many financial institutions are unable to use or manage their data effectively. Some estimates put the amount of data used by financial institutions at 0.5% of that available to them. Worse still, many do not fully leverage the data they do have access to. PwC’s Big Decisions Survey in 2016 showed that only 33% of finance executives would expect to use data and analytics to influence their next big decision.   

It doesn’t have to be this way. Data engineering can empower financial services organisations to overcome customer and regulation-based challenges. For example, data done right can recognise unusual one-off payments into and out of accounts, something that’s key to detecting fraud in its early stages. At the same time, opening up data to employees can not only ensure total access to key metrics but also improve the frequency and quality of collective insights. And that’s not to mention the ways data can be used to track customer behaviour and improve the efficacy and value of interactions with them. 

Data’s value runs deep. A typical financial institution holds vast amounts of unstructured or uncorrelated data. Like an immense iceberg, what we can perceive and process as humans is just a tiny fraction of the whole. And what remains beneath the water has the potential to be a major business booster or disruptor. Financial institutions cannot afford to idly navigate the market landscape, facing fierce competition, without fully grasping their own internal information. 

Data science, artificial intelligence and machine learning are fundamental tools in helping financial institutions distinguish valuable insights from background data noise.  

Critical Software enables the dynamic application of data services to help resolve a range of issues faced by financial institutions, from using smart data analysis to track customer behaviours and optimise fraud detection to implementing data-as-a-service (DaaS) capabilities to enable more efficient data management. Coupled with our data visualisation capabilities that encourage the democratisation of data access throughout institutions, we can implement a well-rounded, no-stone-left-unturned approach towards using data to modernise your organisation.  

Our approach to data focuses on four key areas: 

• Data-as-a-service (DaaS) – sourcing incisive data on an ad hoc basis. 
• Data visualisation – making intuitive dashboards to make it easy to draw insights and make comparisons.
• Smart data analysis – using machine learning and natural language processing, amongst other techniques, to analyse data smartly. 
• Business knowledge extraction – taking clear meanings from data using master/reference data governance, cataloguing, change monitoring and other tools.

We combine our experience of financial services technologies and data expertise with our pedigree in business-critical user experience design to build solutions that are tailor-made for your organisation and your customers’ needs.