Using Machine Learning to Facilitate Fraud Prevention

© MJV Technology & Innovation

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The first step towards a positive impact

Reducing Risk by Automating Fraud Detection

of consumers expect companies to take an active stance on social, environmental, and political issues

According to a survey by "Social Sprout"

Reducing the Risk of Growth

After a change in positioning, a finance institution became more exposed to risk due to an increase in account applications. Previous fraud detection was done manually, and our client needed an automated solution.

MJV was tasked with building an AI model to detect possible fraudulent account applications for the client’s Mobile Payment System.

The First Filter is Always the Coarsest

The client bank already had a layer of fraud prevention in place when it came to sorting through applications. While this process was time-consuming, it was incredibly accurate.

Implementing a profitable strategy in a different market

A High-Fidelity O2O Prototype

MJV provided Pro-Natura with an app experience blueprint, containing the app’s value proposition, as well as feature prioritization for a minimum viable product.

We also provided our client with a complex document detailing the process, steps, and suggestions for prototyping and implementation of the MVP to be tested in the field before being adopted by local harvesting communities.

Today’s market is filled with companies rushing to be the first to adopt AI into their processes and platforms. This AI gold rush can cause many to confuse novelty for effectiveness. We here at MJV always ensure that the solutions we offer our clients are tailored to their challenges.

When we use AI in our projects, we avoid gimmicks and guarantee that artificial intelligence is used intelligently. If you’re a financial institution/insurer trying to prevent fraud, or just a company looking to optimize your processes, why not schedule a meeting with one of our consultants? Come discover what MJV can do for you.

Achieve riskless operations through AI and Machine Learning

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About MJV

MJV Technology & Innovation is a global consulting firm that helps leverage business, foster innovation, and solve business challenges for some of the world’s largest companies.

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25+ years of experience.

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Presence in the U.S., Europe, and LatAm.

A global team with over 1,300 experts, including designers, engineers, anthropologists, data scientists, developers, and marketers.

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The goal of our AI model was to act as a primary filter, ensuring that all fraudulent claims made it to the secondary filter. While this means that a portion of the flagged applications were non-fraudulent, it ensured that fraudulent applications did not make it through to approval.

Validating a New Revenue Model Through Testing-as-a-Service

A solution is only as good as what you can prove, and MJV did this through a testing-as-a-service framework.

While our client ran the stores (both digital & physical), we took it upon ourselves to craft, test, and validate the strategy to determine if it should be implemented nationwide.

Fraud Detection Training and Deployment

Our solution was completed in two phases: AI model training and deployment.

TRAINING:

Validating a New Revenue Model Through Testing-as-a-Service

A solution is only as good as what you can prove, and MJV did this through a testing-as-a-service framework.

While our client ran the stores (both digital & physical), we took it upon ourselves to craft, test, and validate the strategy to determine if it should be implemented nationwide.

Simple & Efficient Fraud Prevention

The first part of the project involved training the model using machine learning.  The learning algorithm finds patterns in the training data which are then analyzed and graded in order to continuously improve the model’s predictive performance.

DEPLOYMENT:

New data is selected and fed to the trained model. The model output is a predictive score indicating the likelihood of fraud (0-40% is an automatic approval, 40-80% passes on to the secondary filter, and 80-100% is an automatic denial). The acceptance threshold can be adjusted by the clisxzent.