Technology vendors such as SAS,
Sybase, Actimize, FICO, and ACL have
produced solutions to meet various
claims fraud management needs within
the claims life cycle:
J SAS applies an enterprise approach to
fraud, waste and abuse detection and pre-
vention by providing a hybrid software
solution. It provides predictive analytics
and neural networks techniques to detect
fraud before claims are paid.
J SAP Sybase IQ aims to reduce financial
cost of insurance fraud, increase speed of
investigation, and improve accuracy of
J Actimize software is able to detect, re-
port and prevent money laundering and
fraud across multiple intermediaries, policy types and data sources.
J FICO™ Insurance Fraud Manager 3. 2
can proactively detect fraudulent claims.
J ACL can highlight suspicious transactions to support fraud investigations and
analyze claims history to identify suspicious transactions and verify compliance
with policy limits.
J Patriot Manager from Aquilan monitors, detects, tracks/manages and reports
insurance-related fraud activities.
J Guidewire and Pega software packages
combine fraud-pattern recognition with
workflow automation to enable response
speed fast enough to prevent fraudulent
claims from being paid.
Focusing on Key Areas
It may be obvious that investments
made to improve the effectiveness of insurance claim fraud management often
involve more than fraud technologies
alone. Critical success factors to effective
insurance claims fraud management technology implementation include focusing
on data, expecting a cleanup investment,
prevention not just detection, and accessing maturity across the claims life cycle.
Q Focus on data. It is critical that
the data-processing environment, data
models, and the data itself not only have
integrity but also are integrated and usable. Effective data governance can improve and prevent data integrity problems from reducing investment value.
The promise of striking claim fraud gold
through the mining of unstructured data
has not yet been realized, but companies
can advance the topic by applying specific BI technologies such as predictive
analytics, sentiment analysis, and others
with an emphasis on large storage and
Q Expect cleanup investment. Very
few insurers that have implemented
solutions to drive a “single view of the
customer” have established data consistency across disparate claim processing systems. Employing effective data
governance and stewardship, effectively
leveraging the mountains of structured
and unstructured data across the organization, or having effectively integrated
systems allow for the support of fraud