Risk & Fraud Analytics, powered by MLOps.
Detect anomalies, prevent fraud, make smarter decisions, and enhance business performance.
Fraud detection and prevention systems need real-time fraud analysis capabilities, using analytics. Risk and fraud management systems must be able to analyse a broad range of data, past and present, to make instantaneous decisions for future organisational gain.
Leading practices can provide a catalyst to help organisations strengthen their fraud risk management program activities, particularly the application and enhanced use of data analytics to identify, validate, and monitor the risks of fraud.
Our AI Analytics and Data capabilities:
Affirmative Scoring, powered by MLOps
How 'Affirmative Risk Scoring' helps organisations to detect risk and prevent fraud
Affirmative Scoring is applied to historical data and real-time online behaviour, to find patterns that can’t be detected manually to predict future behaviour and events. The use of advanced analytics, data science and ML and AI techniques recognise customers that are identified as ‘debt risk’ profiles. Develop a 360° view, engage with your customers more effectively, and increase debt collection through voluntary payment compliance. Early automatic digital nudge techniques can be used rather than reminder letters to reduce cost of debt collection.
How does it work?
The process is underpinned by a multi-pronged approach which fuses innovation, business value, and insights from AI and ML capabilities, and can be applied across industries. Here’s a few examples:
Public sector
Local government: such as council tax risk predictions, predictions of vulnerable adults and children at risk, to enable early prevention strategies.
Healthcare: prediction of high-risk segments, so early interventions can be implemented. Prediction of demand can be based on various parameters: weather, seasonality, pandemic, and other factors.
Immigration authorities: traveller risk profiles to predict risk citizens, helping to reduce crimes and national security.
Private sector
Utilities: predict customer churn, machine failures, predictive maintenance and more.
Retailers: use an amalgamation of data to assess consumers relationships and predict what customers will buy next.
Manufacturers: avoid unexpected and costly machine breakdowns, predict supply planning with higher levels accuracy, and provide a far more exceptional service.
Our approach
Our approach at Invenio is to create an actionable insight driven transformation approach, enabling organisations to manage datasets ensuring accuracy and efficiency. Our methodology automates the entire data analysis workflow, providing more efficient and comprehensive insights. Our experts can help you address unique business challenges, while adding real business value, using our unique Invenio approach:
