All business operations face varying degree of transactional or periodic risks. It is necessary to understand the nature of the risks and put risk mitigation strategies in place. Key sectors like Financial Services,Insurance, Telecom and Government have significant components of financial and compliance risks. It is important to segregate the impact of individual components of risk and understand their impact in isolation and in combination with other factors. Scoring of risk at individual and entity level helps organizations minimize losses and maximize facilitation.
Customers differ in their extent and propensity to inflict real or notional losses to its service providers. The objective of this solution is to determine the risk infliction potential of the customers and pre-empt suitable mitigation steps through marketing or enterprise CRM. The past behavior of the customer along with few dynamic market linked variables like Debt burden rate at individual level are the strongest predictors of future risk. Predominant methodology is logit/ tobit based regression. In specific situations, beta and gamma distribution based risk determination is also utilized. A custom solution is typically required for every risk segment.
While there is an unnecessary operational cost associated with the scrutiny of a non-risky customer transaction (Type 1 Risk), there is also a financial or compliance cost associated with the non-scrutiny of a risky transaction (Type 2 Risk). Organizations with high magnitude of valuable transactions therefore aim to adopt a decision support system that can maximize non-risky facilitations and minimize risky let-offs or in other words can balance the Type 1 and Type 2 risk. The two risk types have different impacts on cost and customer handling policies, and a scientific optimization is mandatory.
There are a variety of fraud related risks like misdeclaration, non compliance, mis representation, misvaluation, misappropriate business benefit claims that confront the insurance, banking and government set up. Historical analysis coupled with methods like neural network help in early diagnosis of fraud, sometimes even at application stages. The solution is useful for business instances where several permutations of rules is possible and sudden gain for entities is possible with minimum concealment or misrepresentation.
The business transactions that require involvement of multiple entities which interact with each other, are under immense likelihood of risky liasioning across entities to mask the root cause. Sophisticated analytical methods help quantitatively compute individual entity's role in past risky transactions as well as their liaison (interaction) impact along with other entities. All future transactions can be tagged for risk depending upon the transaction variables and entities involved.