Fraud Risk Management using Artificial Intelligence
Fraud is an issue that all organizations may face regardless of size, industry or country. The risks of fraud may only be increasing, with growing globalization, more competitive markets, rapid developments in technology, and periods of economic difﬁculty. Organizations are dealing with Fraud risk with sound system of internal controls. But the need is : “When you’re dealing with frauds and liars, listen more to what they don’t say than what they do.” ― DaShanne Stokes
Artificial Intelligence, can play vital role here. Traditional Fraud risk management systems support to mitigate risks to certain level but Fraud Risk Management using Artificial Intelligence will be an added advantage to channelize innovative attacks in right way. Here are some ways of using Artificial Intelligence:
- Data analysis, in the form of continuous monitoring of transactions and controls, is increasingly used as a key component of risk management and audit processes overall. Fraud detection systems should be transformed to investigative tools instead of stand-alone solutions.
- Artificial intelligence techniques will enable Real Time Fraud Detection with proactive monitoring on consumer activities, detection of suspicious devices and revealing hidden relationships, suspicious association among entities.
- The use of artificial intelligence to manage risk is particularly helpful when handling and evaluating unstructured data—the kind of information that doesn’t fit neatly into structured rows and columns. Cognitive technologies, such as natural language processing (NLP), use advanced algorithms to analyze text in order to derive insights and sentiment from unstructured data.
- Machine-learning techniques have matured to a level where they can help to elicit knowledge from historical data sets and can allow systems to adapt to changing environments as new huge unstructured data become available from recent experience. The techniques reported on include adaptive user profiling, unsupervised learning (for example, clustering), various classification and regression models (for example, neural nets, rule learners, decision trees), link analysis, sequence matching, and fuzzy logic.
- Neural networks can detect trends and patterns other computer techniques are unable to.
- Case-based reasoning solves new problems using past experiences in solving similar problems. Previous cases and outcomes are stored and organized in a database. When a similar situation presents itself again later, a number of solutions that can be tried, or should be avoided, will present immediately. Solutions to complex problems can avoid delays in calculations and processing, and be offered very quickly.
- Decision support scenarios needed for fraud problems are more complex than the simple classification problems. Combining AI and ML techniques improves fraud detection and management capabilities.
- Unsupervised and supervised learning for Hidden Relationship and Out of pattern analysis – Comparing customer behavior with peer group behavior and also with customer’s past behavior.
- Combine data mining, profiling, and classifier learning;
- Use link analysis to identify and define entities – Identifying other entities associated with known types of fraud, as well as practices used by fraud-linked entities – sometimes using analysis of social networking activity and developing strategies to counter these practices.
- Model Development – Creating fraud scoring tools and detailed statistical analytics to provide quantitative insight into possible fraud activity
- Rule Development – Creating and applying rules for basic business activities to spot unusual trends and specialized rules for specific transactions.
- Sequences of actions, rather than individual actions, to examine context and infer fraudulent intent.
Artificial Intelligence to Fraud Risk Management is a Game Changer and many organizations including top industry players like Deloitte, Capgemini have solutions in practice to leverage AI techniques to anticipate and proactively manage risk.