The volume of data available today is more than it has ever been.

By applying Machine Learning and Artificial Intelligence processes to our client’s data, we are able to uncover hidden relationships from vast troves of data and organisational intelligence to generate insights that catalyse growth.

The difference from traditional data analysis is that we use data to improve performance or inform predictions.

Our analytic products can be descriptive, diagnostic, predictive or prescriptive, depending on the specific use case or need.

We can help describe patterns in data (how one factor affects another) or use that relationship to predict a future state (how a change in one factor will affect another).

Data is inherently complex.

Our team of data engineers are able to bring data to life through the development of interactive dashboards. 

The data we show can range from raw unstructured data to the product of our data analyses.

We can show KRIs and KPIs, operational monitoring and reporting (including IoT integration), marketing campaign effectiveness monitoring and predictive scenario planning.

We are solution agnostic, and so whatever the data or the platform, we can build a solution that fits with our client’s needs.

Everything happens somewhere and all things are related, but near things are more related than distant things.

Having adopted these two principles, we understand that location is a key factor in unlocking patterns in data.

With the rise of locationally aware technology – mobile devices, location sensors, social media, IoT devices, remotely sensed images and more –organisations have access to spatial data far more readily and in much larger volumes than in the past.

By using geospatial analytics and visualisations in conjunction with our other data analysis techniques, we can unlock pattens in data that were previously hidden away in rows and columns including proximity, distance, contiguity & affiliation.

We also deploy our deep knowledge to assist our clients in realising the full potential of geospatial technologies within their organisations – from policy and process development to the inclusion of location in their business practices from asset management and business planning to metaverse and digital twin implementations.

Unlike other analytic processes, Forensic Investigation is about detecting patterns that fall outside the norm, meaning finding those patterns is becoming more complex as volumes and data types grow.

Our specialists have a range of analytic techniques at their disposal to aid in their investigations:

• Network mapping to explore relationships between people
• Unsupervised modelling to identify suspicious deviations from normal behavior
• Supervised modeling to articulate similarities between groups of historical fraud patterns and identify what separates them from the normal patterns
• Text and computer vision analytics to sift through vast volumes of communication, including emails and audio to discover patterns in data such as repeated phraseology between previously unconnected parties.

Financial Crime is becoming more complex and harder to detect.

By combining our Data Analytics specialists with our deep Financial Crime SMEs we can build models to answer questions on fraud, bribery and corruption, money laundering and sanctions.

We can deploy these models both for historic detection, but also to continually improve surveillance systems and processes.

By using our knowledge of surveillance models, we can assess our clients’ current surveillance technology, including testing of rule-based systems and running data-driven assessments of current surveillance processes, and enhancing them if appropriate.