Big Data for Fraud Prevention

Big data analytics is an effective solution for identifying behavioral patterns and establishing strategies to help detect and prevent fraud in various business sectors.

Monday, September 6, 2021

Most companies are not aware of the information they have and how to leverage, analyze and understand it, which can result in the loss of a large amount of potentially useful data by normalizing fraud and other criminal activities in their processes and make them difficult to prevent and detect.

Also read: "Walmart Vs. Soriana: Consumer Foot traffic Analysis"

Fraud detection through Big data analysis, data mining and machine learning models uses trends, patterns and behaviors to detect and prevent suspicious activities in purchasing processes, credit activities, accounts or transactions, internal and external processes, among others. This makes possible to automatically detect fraud and allow organizations to consolidate, map and normalize large amounts of data that can be effectively analyzed to design strategies that detect and establish connections between anomalous trends, notice a cybernetic attack or mark a security breach.

Here are some of the general benefits of the implementation of fraud analytics in businesses:

  • Identify irregular and unusual patterns, business problems or risk areas where activities or processes may result in fraud. 
  • Saves costs and maximizes revenue.
  • Detects anomalies across channels, comparing data from different information sources to find discrepancies, such as social networks, databases, call centers, etc.
  • Predicts suspicious activity before it causes damage to an organization's assets or goods.
  • It provides an internal view of processes and identifies where there's more opportunity for fraud activities by creating strategies that are better adapted to the operations of a business.

You may be interested in: "Location analytics can drive retailers to success: Case Study Home Depot Vs. Ace Hardware."

As specialists in data analysis, at PREDIK Data-Driven we identify anomalies in payments, fraud problems in systems or processes, test the effectiveness of cybersecurity controls, identify and predict patterns and behaviors in internal and external data sources processes where fraud can or is more common, among many others.

¿Busca soluciones de inteligencia comercial para su empresa?

Do you need help to detect and prevent fraud operations within your business processes? contact us!

this site is protected by reCAPTCHA and Google's privacy policy and terms of service.
Need assistance? Contact us
(506) 4001-6423

More on this topic

Retail analytics: PETCO Vs. PetSmart

November 2021

Pet shop owners need to apply location intelligence techniques and foot traffic analytics to identify consumer mobility patterns, in order to maximize sales and generate more efficient expansion models.

The correlation between foot traffic, sales, and the success of pet shops franchises have been studied and proven, so the development of this type of analysis has become a priority in the site selection process and expansion modeling.

Analytics to understand customers' behavior patterns

September 2021

Customer behavior analysis it's important because it helps businesses to understand how users interact with a brand, now it can be done in a more objective and real way using Big Data management techniques

Customer behavioral analytics is the process of collecting and analyzing data, using technology tools such as Big Data, machine learning and location intelligence, help gain long-term insights of the average purchase value, customer lifetime value and users interaction with a brand, enabling companies to incorporate data-driven business strategies that facilitate decision making and revenue maximization.

Which is your company Data Maturity level?

September 2021

Data has proven to be a competitive differentiator in different business sectors. The performance of any organization is highly correlated to the maturity of its data, so it's important to know in which level you are in this process.

What is Data Maturity?

Retail: Benefits of Data Monetization

September 2021

More and more retail organizations are monetizing their data to increase revenue, boost productivity and optimize costs, enabling effective leveraging of assets, technology tools and external information to generate better results.

In the retail sector, data monetization is about making better informed decisions, increasing revenue and reducing costs from access to different types of stored, categorized and accessible data.