Use Cases

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Fraud Detection

Fraud Detection Analytics: Finding the Hidden Threat

Whether internal or external, there are a wide variety of threats posed to enterprises across multiple industries. The most difficult threat to diagnose & address, however, is fraud. Fraudulent activity is a high-cost threat that can compromise the integrity of your company as well as cripple your bottom line. Fraud can take the form of internal activity, such as an employee modifying financial records, or can arise from an external threat, such as customer credit card fraud. In either case, the use of fraud detection analytics using predictive data science methodologies enables companies to discover potential fraudulent activity before it occurs.

Fraud Detection Techniques: Making the Connections

Fraud prevention isn’t just about basic regressive analysis. On the contrary, it’s about connecting the data points to discover potential fraudulent behavior before it happens. This starts with finding interactions between products, locations, and devices and then mapping those data points to individual users, customers, and/or employees. This approach effectively connects together vast quantities of knowledge with all of the people who somehow interacted with that knowledge.

The wide variety of threat types and varieties pose a significant challenge for fraud detection solutions. Disgruntled employees and criminal elements are continually using more advanced techniques to siphon revenue away from companies; their methods can be straightforward, such as a staged car accident, or more nuanced, such as using accounting irregularities to mask embezzlement schemes. Given the complexity involved, fraud detection techniques used in predictive analytics need to excel at creating connections from raw data and then discovering which interactions convey potential fraudulent behavior. Creating all of those connections from raw data is the job of Data Science Studio (DSS).

Using DSS for Fraud Detection Analytics

A significant aspect of fraud detection lies in discovering abnormalities: events that, when compared to typical behavior, simply do not “fit in.” The use of fraud detection software enables your organization to discover the triggers and situational interactions that are likely to produce fraudulent activity — this level of intelligent prognostication, based on comprehensive data analysis, is the key to empowering your company to stop fraud in its tracks while saving both time and money.

DSS enables your organization to quickly prototype the complex data transformations required to create highly-effective fraud analytics models. Anti-fraud modeling is based on a premise of seeking complex patterns in event sequences. In other words, discovering abnormal patterns of human behavior in a sea of complex object-user relationships. A wide and varied data net enables DSS to consider all possible factors, with user/customer history being a prominent ingredient within that mix.

Fraud prevention using DSS is possible due to:

  • Capability to accept a wide variety of fraud-based data from multiple sources without regard to size;
  • Collaborative environment that automates labor-heavy fraud data tasks, such as cleansing, formatting, parsing, etc.;
  • Intuitive interface that gives you the tools you need to easily create & test the best-fitting anti-fraud models based on the size and complexity of your data;
  • Robust visualization functionality that empowers your teams to produce rich graphical charts & visualizations that convey exactly how fraudulent activity may occur in the future.

From our Blog: Detecting Fraud in Medicare Data

A Flexible Way to Build Your In-House Fraud Detection Solution

Data Science Studio is a powerful solution in your fight against fraud because it’s centralized and can seamlessly integrate with existing solutions. DSS is designed as a singular environment that supports all facets of data exploration, machine learning, model creation, and visualization. Support of multiple data source connections means that your fraud detection solution can include a wide variety of fraud-related data, such as financial reports, electronic health records, and claims data — supported datastore types include Excel, CSV, SAS, JSON, SQL, and many more connector varieties. DSS is also capable of executing data transformations on large-scale infrastructures (e.g., Hadoop), effectively leveraging Big Data analytics at a reasonable cost.

When it comes to analytics it’s not just about raw algorithmic performance — your fraud detection software needs to seamlessly integrate with existing infrastructure, as needed. Although DSS can be used as a standalone solution, it is more than capable of integrating with external apps, such as a Case Management System (CMS). DSS facilitates this integration by providing you with transparent algorithms that you can export and integrate with existing fraud detection rules.

Fraud Detection Use Cases: Industry-specific Challenges

Fraud Analytics in the Healthcare Industry

  • Challenges: Increased cost of providing insurance benefits to employees, genuine data compromised, patient exploitation, higher premiums and out-of-pocket expenses for consumers
  • Data: Wide variety, from electronic health records to accounting data
  • Typical Use Cases: Patient billing analysis to determine claim fabrications, financial records and behavioral analysis to determine identity theft, surgical procedure analysis to determine unnecessary services
More Healthcare Analytics Use Cases

Fraud Analytics in the Insurance Industry

  • Challenges: Ability to obtain accurate data, cost vs benefit of fraud investigation process
  • Data: Insurance claims, interaction data (social media, cell phone, ATM usage), financial records, police records, hospital records
  • Typical Use Cases: Insurance claim analysis to predict potential fraudulent claims, injury data analysis to predict likelihood of insurance policy fraud, public sector analysis of fraudulent claim data (e.g., arson, property, auto) to predict areas prone to fraudulent activity (e.g., for premium determination/adjustments)
More Insurance Analytics Use Cases

Fraud Analytics in the Financial Securities Industry

  • Challenges: Determination of data accuracy, loss of investor confidence, compromise of corporate reputation, financial loss for company and its investors
  • Data: Publicly available financial records, corporate records, banking transactions and transfers
  • Typical Use Cases: Security price analysis to determine likelihood of authentic vs inflated valuations, corporate financial records analysis (assets, liabilities, costs, revenue) to predict securities fraud
More on Advanced Analytics in the Financial Securities Industry

DSS for Fraud Analytics... in a Nutshell

As a robust predictive analytics solution, Data Science Studio surpasses typical solutions branded as fraud detection software. DSS is a holistic and collaborative platform that addresses the broader spectrum of analytics — the core technology is capable of connecting to a wide variety of datasets and its inclusive approach allows for participation by multiple skill profiles. The end-result is a platform that can not only be used for fraud prevention, but for a wide variety of applications across both industries and departments.

User Feedback

“With DSS, we built an app in only 3 months that enabled a 30% boost in our team’s productivity. We were able to adopt Hadoop and Machine Learning faster than we had anticipated.”

Erwan Pigneul

Project Manager - Pages Jaunes

“We appreciate Data Science Studio’s capacity to handle important volumes of data, and the tool’s openness towards the functioning of the algorithms."

Yves-Marie Pondaven

CTO – Parkeon

“Before DSS, we'd externalise the whole research phase that leads to concrete solutions. Our work mainly consisted of descriptive analytics on past data. With DSS, we have internalised the design & deployment of our data solutions. We can now predict the future actions of our customers and act accordingly."

Damien Garzilli

Strategy and BI Manager – Showroomprive

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