BI can lay stepping stone for Fraud Analytics!

Fraud losses can impact every business. Fraud Analytic differs from Business Intelligence(BI) type Analytic with relatively higher human interaction and deep dive, but it's all about data. Understanding of data and visual representation can provide early alert, enabling users to take timely action to stop fraud and halt losses. A proactive approach will combat fraudulent behavior and increase the perceived value of the organization and help to the cause of Customer loyalty, competitive edge in dynamic market place, merger and acquisition so on so forth ...

Information Life-cycle and evolution: Yes, Information has a lifecycle. Information derived from data should be following the information supply chain. Data becomes information when it represents business relationships. Data should be aligned to business model and process such that precise insight can be represented of customer behavior, how much acceptable risk can be taken and so on. Organization of information should be able to cater to wide range of strategies covering definition, policy, infrastructure and operation and functionality. For example - Data is born when customer places an order, which gets associated to identification definitions like customer, product, market .. Then goes thru order fulfillment and then customer service repair so on so forth. The data in the meantime goes through various transformations as it is related to financial, marketing, demand planning or predictive uses, in order to answer questions crucial to operating and optimizing business decisions. Finally, information has an end game. The customer moves away or the product is discontinued. The data is no longer updated, remains unused by the business and eventually become irrelevant both to the enterprise and the society in which the enterprise does business. As the volumes of data accumulate, the data warehouse becomes “obese.” Meanwhile, the data warehouse become entwined with mission-critical systems, impacting the performance of both transactional and decision support systems. This drive information mining with stale information and more resources to deep dive into a slice of information set for confirming the outliers found in the initial data set.

Managing Your Data Growth: A system that has been in production for several years is likely to contain a significant volume of data that is not used at all or used infrequently. Data warehouses often start big and get even bigger. The lifecycle of the data warehouse and the requirement to perform archiving shifts into the foreground. Enterprises engage in data archiving as part of an approach to information lifecycle management, of which data warehousing is an essential part. Archiving is the best way both to improve performance of the data warehouse (or transactional system) and to satisfy the requirements for data retention and security. This will enable improved ROI, information richness and better response when reaching for active and/or inactive data.

Visual representation of Information for Discovery: The human brain is good at doing some things and limited in others. For example, our brains is good at recognizing visual patterns, while they are able to remember relatively little from large amount of information. Primarily data analysis is making sense using comparison as individual facts mean nothing by themselves. Facts become meaningful when we compare them to one another. By displaying data in series of small graphs arranged as a visual cross-tab, which allows multiple dimensions to be compared simultaneously. This will allow users to see and compare patterns and trends of outlier or inconsistent behavior. Thus provide potential fraud candidate for further investigation.

Conforming Architecture: Fraud identification has a different approach than general Data warehouse or BI solution. In case of general BI solution, the measurement criteria is often a set of transaction quantifying success of a campaign or Sales Measures so on, however fraud would taking a subset of data and analyze association with scenarios by means of data and deep dive. As BI can provide model based information of trends, human interaction will identify the outlier cases for further investigation. Mature Business Intelligence architecture need to consider the commonalities and differentiators in their architecture to cater to these different audience needs. In addition, the deployment design of information architecture if not flexible enough then there will be high cost of reaching to the tip of the iceberg beyond that potential could be more investment for each scenario i.e unsustainable spiral.

Holistic approach: Fraud Management Lifecycle is dynamic, evolving, and BI solution architectures should be flexible enough to adaptive it. Effective fraud management requires a balance in the competing and complementary actions within the Information Lifecycle. Solutions can be defined based on past data trend, but the power of success lies with solutions that can embrace the new data to provide the insight.

Consolidation has left a lot of companies with multiple incompatible systems, inconsistent applied policies, more holds and less penetration in dynamic market place. Even with mature BI organization, fraud analytic can only be effective with ability to efficiently link with different data set and robust architecture.

Reference: Journal of Economic Crime Management
Statistics: The Fraud Management Lifecycle Theory