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Back to the Future

Taking a timely theme and looking at the way in which fraXses is helping organisations to move from the “rear mirror” view of static reporting, swiftly through the current ability to deliver real-time, streaming data analysis and into the predictive and prescriptive analytics.  

Descriptive analytics: means looking at historic data, ranging from 1 minute ago to years ago. It can […]

By |October 21st, 2015|Analytics|

Maximising revenue by keeping planes off the ground

Challenge:

Airlines need to identify the ideal times to fly to maximise demand, match capacity to demand and meet all operational requirements.  While at the same time establishing how to design their flight schedule in such a way as to maximise revenue.  With an almost infinite number of possible combinations, this process is inefficient and leads to higher costs and […]

By |October 20th, 2015|Algorithms, Data, Machine Learning, Pattern Recognition|

Optimising sales pipelines

Problem:

Without a doubt, the current environment is extremely challenging for sales teams in every industry and region.  Enabling them to react quickly to changing sales conditions could literally mean the difference between overachieving the quota or failure.

Knowing exactly what is happening in your sales pipeline and being able to respond quickly to changes as they appear is critical for every business. […]

By |September 20th, 2015|Analytics, Data|

Stopping the fraudsters

Problem:
For insurance companies, the fraud can range from the use of stolen credit cards, collusion and identity theft. According to the Insurance Fraud Bureau (IFB) in the UK, undetected general insurance claims fraud total £2.1billion a year adding on average £50 to the annual costs individual policyholders, on average, each year.

A growing phenomenon is the “Cash for Crash” scam in […]

By |August 20th, 2015|Fraud, Insurance, Pattern Recognition|

Visualisation libraries

Enterprises are beginning to realise the important role “big data” plays in achieving business goals. Concepts that used to be difficult for companies to comprehend— factors that influence a customer to make a purchase, behaviour patterns that point to fraud or misuse, inefficiencies slowing down business processes—now can be understood and addressed by collecting and analysing big data.  The insight gained […]

By |July 20th, 2015|Analytics, Data|

Pattern recognition for cross selling

Up-Sell/Cross-Sell Recommendations
Providing up-sell and cross-sell recommendations to customers is the mostly widely adopted big data use case in the retail sector. This enables retailers to increase online purchases by recommending relevant products and promotions in real time. Retailers can recommend products based on what other similar customers have bought—providing upsell, cross-sell or “next best offer” opportunities.

fraXses provides real-time capabilities that […]

By |June 20th, 2015|Machine Learning, Retail|

Gambling behaviour analysis

Working in both the online and actual casinos we help the operators work with the vast volumes of data generated through the gaming activity.

We predict players that are going to leave the casino before they actually leave. With this information, the marketing team can intervene with a retention strategy before their players leave.

We also have the ability to predict how […]

By |May 20th, 2015|Algorithms, Gaming, Machine Learning|

Stress Testing

Problem:
The financial turmoil in the last few years have led and are still leading to strict regulatory requirements for Risk Management in Banking. For assessing the impacts of various economic and political worst case scenarios banks are forced to establish a stress-testing framework for their overall risk position including credit risk, market risk, liquidity risk, operational risk and other risks […]

By |April 20th, 2015|Compliance, Financial Markets, Regulatory|

Electronic Communication Surviellance

A recent swathe of trading scandals has spurred big banks to turn to new technology and data sources as they attempt to crackdown on illegal behaviour by their staff.  Solutions are becoming more sophisticated with algorithms and artificial intelligence being used to identify patterns of speech and networks of contacts as opposed to merely catching keywords.  A lot of times, […]

Retail Fraud Detection

Retail fraud can range from fraud in returns or abuse of customer service, or credit risk for larger purchases, based on, for example, uncovering fraud rings, social media activity of customers and detecting patterns. It can also be major security breaches putting private customer information at risk. Retailers need to protect their margins and their reputations by proactively detecting fraudulent […]

By |February 20th, 2015|Fraud, Retail|