AXCESS Financial Finds & Stops Fraud with Interaction Analytics
公司规模
1,000+
地区
- America
国家
- United States
产品
- CallMiner Eureka Interaction Analytics
技术栈
- Interaction Analytics
实施规模
- Enterprise-wide Deployment
影响指标
- Cost Savings
技术
- 分析与建模 - 预测分析
适用行业
- 金融与保险
适用功能
- 商业运营
用例
- 欺诈识别
服务
- 数据科学服务
关于客户
AXCESS Financial is a consumer financial services firm with 3500 employees that support its retail and online operations. The company is a frequent target for fraudsters that try to trick employees into transferring money through bogus transactions. The fraudsters were successful an average of about once a week and were costing the company more than $360,000 annually. AXCESS Financial effectively solved the problem and stopped the losses by creating a new use case for interaction analytics.
挑战
AXCESS Financial, a consumer financial services firm, was a frequent target for fraudsters who tricked employees into transferring money through bogus transactions. The fraudsters were successful an average of about once a week and were costing the company more than $360,000 annually. The company knew it was getting scammed, but didn’t always know how, or even who the perpetrators were. A typical fraud investigation would start when a store associate felt a customer interaction was suspicious. The process was time consuming and often only began after AXCESS Financial had been defrauded. The process had little preventive value, and left many attempts unreported and uninvestigated. AXCESS Financial had between 35,000 and 40,000 customer contacts in a typical day and over 60,000 during peak periods, so some suspicious activity inevitably fell through the cracks.
解决方案
AXCESS Financial had already implemented the CallMiner Eureka interaction analytics solution to improve the quality and compliance of its contact center operations. It had very good success with automating quality assurance audits, and at identifying effective agent behaviors and improving agent performance by using the automated scorecards that the analytics solutions produced. The company wondered if it could create similar “scorecards” to identify potentially fraudulent calls. Eureka monitors calls and other interactions for specific language used, behaviors and patterns that match best practices. Keown thought these monitoring capabilities could be applied to detect fraud. Based on this analysis AXCESS Financial identified two common approaches, which likely indicated two different fraud rings were targeting the company. It then built a scoring system to quantify the risk associated with a call, based on different language and behavior categories of the caller. The potential score range was 0 to 600, the higher the score, the higher the likelihood the call was fraudulent. AXCESS then tested its scoring system against its analytics system’s records of past calls (especially those that were later found to be fraudulent) and found the scorecards were an accurate predictor of fraudulent activity.
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