Data: As with all ML applications, quality data is foundational to building anti-fraud ML systems. Data sets are only growing larger, and as the volumes increase, so does the challenge of detecting fraud. Thankfully the adage that more data equals better models is true when it comes to fraud detection. The make-or-break factor is having a ML platform that can scale as data and complexity increase. Multiplicity: There's no single ML algorithm or method that works best for fraud detection.
All things change, and you must adapt over time. Ongoing monitoring of machine learning fraud detection systems is imperative for success. As populations and the underlying data shift, expected system inputs degrade and therefore have an impact on overall performance. This isn't unique to machine learning systems; rule-based systems have the same challenge. But newer machine learning methods can adapt to new and unidentified patterns as underlying changes occur. This eliminates some, but not all, of the machine learning retraining and evaluation steps.
The digital revolution has changed the healthcare landscape irrevocably. Patients expect faster, more efficient care that costs less, which is where artificial intelligence (AI) can help. AI and machine learning allow healthcare organizations to evolve and keep up with trends and new methodologies. Data science enables systems to ingest massive quantities of information quickly, to generate insights and predictions that allow healthcare organizations to focus human attention on what's really important: providing quality care. One of the techniques that are essential for data teams, physicians, insurance analysts, etc., in healthcare to understand is anomaly detection.
Machine learning has become an invaluable tool in the fight against fraud. It combines computational statistics, artificial intelligence, signal processing, optimisation, and other methods to identify patterns. Machine learning has been a significant breakthrough in helping companies move from reactive to predictive by highlighting suspicious attributes or relationships that may be invisible to the naked eye but indicate a larger pattern of fraud. The great value of machine learning is the sheer volume of data that computers can analyse that humans cannot, thanks to a variety of pattern recognition algorithms. With this you can add exponentially more data to your analysis -- but selecting the right data and approach to model the problems is critical.
The greatest challenge when talking about artificial intelligence/machine learning is actually in understanding what data sets we are looking at, and what model/combination of models to apply. Amazon's Machine Learning offering is one example of an automated process which analyses the data and automatically selects the best model to use in the scenario. Other big players who have similar offerings are IBM Watson, Google and Microsoft. Provenir's clients are continually looking at new and innovative ways to improve their risk decisioning. Traditional banks offering consumer, SME and commercial loans and credit, auto lenders, payment providers and fintech companies are using Provenir technology to help them make faster and better decisions about potential fraud. Integrating artificial intelligence/machine learning capabilities into the risk decisioning process can increase the organization's ability to accurately assess the level of risk in order to detect and prevent fraud. Provenir provides model integration adaptors for machine learning models, including Amazon Machine Learning (AML) that can automatically listen for and label business-defined events, calculate attributes and update machine learning models. By combining Provenir technology with machine learning, organizations can increase both the efficiency and predictive accuracy of their risk decisioning.