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Fraud detection: predict and prevent fraud by identifying actions and reducing costs

Fraud detection helps predict and prevent fraud and reduce costs by taking specific actions.

Overview

Alongside increased risk associated with lending, banks have witnessed growing fraudulent behavior. This behavior may be internal (by undisciplined staff) or external (by fraudulent customers). In the insurance market, the incidence of fraudulent events has grown, especially in certain geographical areas.

Overt fraud is known to be low, but suspect cases and claims that are resolved, for example, by settlement between the counterparties, are significantly higher. Lack of control over such events can lead to over time and (sometimes sizeable) losses. Businesses do not have the right information needed to tackle a variety of fraudulent situations. It is crucial for fraud managers to have as much information as possible to spot fraudulent and new abnormal behavior early on, and to identify possible fraudulent networks of people among counterparties, dealers, and other parties involved in the business.

Fraud Detection is designed to help organizations reduce fraud-related costs. The application’s predictive capabilities combine different techniques, mixing in a single risk score the business user experience with predictive modeling techniques and anomaly detection models.

Why Accenture

Financial Services applications are built within the scalable Accenture Analytics Applications Platform that provides fast and easy access to an array of industry and functional applications that bolt on to the platform, allowing quicker time to market and more rapid results for clients.

Clients also access our wide range of capabilities rooted in

  • Industry knowledge. The validity of our advanced analytics outcomes is underpinned by our deep knowledge of the sector.

  • Business. Applications are designed for business users and focused on business results, minimizing advanced analytics complexity. Getting to accurate outcomes does not require statistical, mathematical or IT knowledge—just business know-how.

  • Flexibility. Applications are based on a framework that can be easily integrated in an enterprise operational environment to drive business process action.

Specific Services

Fraud Detection screens all claims procedures, loan applications and product purchase procedures allocating a risk score to each that enables the fraud manager to set up alert logics for receiving signals based on its control objectives.

Users can assess all procedures based on certain business rules that are specific to individual industries such as insurance, consumer credit and lending products. Through predictive analytics, users can define fraud prediction models based on past cases of overt fraud and, even more so, on cases deemed suspect, thereby capitalizing on the value of all available information.

Finally, the Fraud Detection application offers SNA (social network analysis) to perform exploratory analyses of dealers and counterparties and enable those in charge of fraud to investigate and recognize abnormal or fraudulent networks.

The application produces risk scores that may relate to individual customers or other actors in the chain, such as branch offices, agents and liquidators, in the case of insurance. The application identifies fraud by analyzing real-time data that are produced every day through transactions and customer interactions with the company. Fraud Detection is able to handle big data as in the case of data from the “black boxes” installed in cars from insurance companies.

The application interacts with company core processes through alerts and reports or triggers that can activate and / or modify the behavior of business users involved in the process, such as a bank counter operator or insurance liquidator.

In this way, advanced analytics benefits spread to all levels of users, even those without skills needed for using complex analytical tools.

Key features

  • Real-time checking and scoring

  • Alerts for claims investigators

  • Alert, email and report management based on deterministic rules

  • Mapping of actions (initiation of disputes, inspections)

  • Overview and breakdown features for anomalies detected / risk level

  • Deterministic rules to identify fraudulent behavior, false claims and risky subjects (customers, employees, companies, third parties)

  • Predictive fraud detection algorithms to improve accuracy in each risk scoring activity

  • Specific rules based on process, branch and claim type

  • Risk scores based on a risk matrix: best of predictive, best of industry knowledge

  • Anomaly detection for potentially fraudulent patterns

  • Predictive models for customers or transaction risk score

  • Profiling of the types of actions and relationships between subjects via Social Network Analysis

  • Analysis of relationships between counterparties to identify fraudulent networks and collusions