Business Context
A crucial factor to the survival and evolution of organisations is to harness the power of Intelligent Automation.
Intelligent Automation has infinite potential in government, healthcare, retail and finance, specifically Machine Learning, which can discover patterns to make predictions. Chief Data Officers and Chief Information Officers can leverage this technology to predict healthcare demand and supply, determine deficiencies around service quality and provide predictive data on fraud.
Our client, International Personal Finance, harnessed the power of Machine Learning for fraud detection and their model now provides them with a clear data intelligence picture and predicts future fraud patterns, enabling them to stay ahead of the threat.
The Client
IPF is a leading international home credit and digital provider of consumer finance. They have 1.7m customers and have issued £772.2m in credit.
The Situation
- Legacy systems unable to optimise fraud analysis across 11 countries
- Early detection and analysis of millions of rows of data in seconds
- Pre-determined models that could not stay ahead of the curve and evolve to predict changing patterns in fraud behaviour
- The criticality of creating a sophisticated and optimal fraud detection system which itself acts as a deterrent for agents
The Challenge
Need to migrate from outdated systems to elevate care standards and streamline processes
With an aim to drive better experiences for its patients and physicians, TeamHealth wanted to modernise its technology infrastructure to:
- Optimise performance and improve process efficiency
- Unify data and generate a single source of truth to upgrade reporting and overall visibility
- Create a seamless authentication experience and add a security layer while accessing cloud applications
- Ensure the ability to navigate future challenges with flexibility, scalability and agility
- Optimise performance and improve process efficiency
- Unify data and generate a single source of truth to upgrade reporting and overall visibility
- Create a seamless authentication experience and add a security layer while accessing cloud applications
- Ensure the ability to navigate future challenges with flexibility, scalability and agility
The Solution
- Machine Learning implementation
- Detection of different types of fraudulent activity
- Business adoption within the SME community
- Correction of business model assumptions by co-locating SME and Mastek Analyst
- Migrate 20 on-prem applications, 23 Essbase Cubes, 4000+ reports and 28 integration touchpoints to the cloud
- Migrate 100 GB of data from legacy systems to Oracle Cloud
- Gain a bird-eye’s view of operations by incorporating dashboards
- Drive better insights from data, improved reporting, top-notch data security, governance and compliance
The Solution
- Machine Learning implementation
- Detection of different types of fraudulent activity
- Business adoption within the SME community
- Correction of business model assumptions by co-locating SME and Mastek Analyst
- Migrate 20 on-prem applications, 23 Essbase Cubes, 4000+ reports and 28 integration touchpoints to the cloud
- Migrate 100 GB of data from legacy systems to Oracle Cloud
- Gain a bird-eye’s view of operations by incorporating dashboards
- Drive better insights from data, improved reporting, top-notch data security, governance and compliance
Mastek’s Machine Learning solution has been transformational—we have increased fraud detection by 15%.
– Amy Foster
Group Head of IT Change, International Personal Finance.
"Mastek’s Machine Learning solution has been transformational—we have increased fraud detection by 15%."
– Amy Foster
Group Head of IT Change, International Personal Finance.
The Outcome
Shaping the Future for Netflix
With Volteo Edge’s intelligence coupled with IoT expertise, Netflix achieved –
- Reduced the time taken to maintain the model from days to just minutes freeing up Analyst time
- Identified 80% of fraud cases
- Increased fraud detection by 15%
- Enabled 25% faster strategic global rollout
- Reduced the time taken to maintain the model from days to just minutes freeing up Analyst time
- Identified 80% of fraud cases
- Increased fraud detection by 15%
- Enabled 25% faster strategic global rollout
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