# Merchant Monitoring
source: https://developer.mastercard.com/brighterion/product/merchant-monitoring/index.md

### Overview {#overview}

###### Help merchant service providers balance revenue growth with fraudulent merchants by assessing the risk of ongoing and newly onboarded merchants {#help-merchant-service-providers-balance-revenue-growth-with-fraudulent-merchants-by-assessing-the-risk-of-ongoing-and-newly-onboarded-merchants}

Financial institutions (FIs), including acquirers, payment service providers and payment facilitators, are under more pressure than ever as e-commerce transactions increase and fraudsters become more sophisticated. Risk management solutions are needed to help balance revenue growth with merchant risk for new and ongoing merchants. With Mastercard's Merchant Monitoring solution, your analysts will work efficiently with fewer false positives and spend more time focused on high-risk cases.

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### Benefits {#benefits}

Increase profits and strengthen customer relationships
![Easy onboarding](https://static.developer.mastercard.com/content/brighterion/uploads/easy_onboarding.svg)

###### Easy onboarding {#easy-onboarding}

Experience a light lift with minimal effort and data requirements
![AI-powered risk scoring](https://static.developer.mastercard.com/content/brighterion/uploads/ai_empower.svg)

###### AI-powered risk scoring {#ai-powered-risk-scoring}

Empower your fraud analysts with accurate risk scores
![Transparency](https://static.developer.mastercard.com/content/brighterion/uploads/transparency.svg)

###### Transparency {#transparency}

Be transparent with innovative explainable models
![Efficient](https://static.developer.mastercard.com/content/brighterion/uploads/efficient.svg)

###### Efficient {#efficient}

See improved results with a solution optimized specifically for monitoring merchant risk

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### How it works {#how-it-works}

Providing merchant risk scores either in real-time or batch, the scoring solution can be complemented with Rules and Case Management modules in addition to Business Insights.

###### Step 1 {#step-1}

Integrate data flow via API

###### Step 2 {#step-2}

Submit data to initialize model

###### Step 3 {#step-3}

Receive scoring via API

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### Use cases {#use-cases}

Examples of how merchant service providers use Merchant Monitoring to find risky merchants early.

###### Screen new merchants at onboarding {#screen-new-merchants-at-onboarding}

A payment facilitator signing up small e-commerce sellers scores each application before approval. Applications with a risk profile similar to known fraudulent merchants go to an analyst for review, while low-risk merchants are onboarded without delay.

###### Detect changes in existing merchant behavior {#detect-changes-in-existing-merchant-behavior}

An acquirer monitors its merchant portfolio for sudden changes, such as a spike in transaction volume, a jump in average ticket size, or rising refund and chargeback rates. These can be early signs of bust-out fraud or transaction laundering.

###### Focus analysts on the highest-risk merchants {#focus-analysts-on-the-highest-risk-merchants}

A risk team uses merchant risk scores with Rules and Case Management to prioritize alerts, so investigators spend less time on false positives and more time on high-risk cases.

###### Customer example: Worldpay {#customer-example-worldpay}

Worldpay increased fraud detection and prevention by 3x and experienced 20x fewer false positives using Brighterion AI for merchant risk monitoring, while reducing fraud by more than $50 million year over year in the US since 2019.

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### Why use it {#why-use-it}

Financial institutions need the right tools to protect themselves and their customers in a dynamic fraud environment while providing a frictionless experience.

### 85%

of senior payment professionals say fraud detection and prevention is the top use case for AI in payments

The Payments Association, Navigating the Rise of AI-enabled fraud, December 2024

### $40 billion

in fraud losses projected by 2027 due to generative AI, up from $12.3 billion in 2023

PYMNTS/NCR, Stopping Bank Fraud with Cybersecurity Solutions, June 2023.

### 71%

of large financial institutions plan to initiate or improve existing fraud solutions by utilizing solutions that leverage AI or machine learning to combat fraud

Deloitte, Generative AI is expected to magnify the risk of deepfakes and other fraud in banking, May 2024

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### Key features {#key-features}

![Rule management](https://static.developer.mastercard.com/content/brighterion/uploads/rule_management.svg)

###### Rule management {#rule-management}

Manage rules with defined patterns (long-term, geo-location, etc.) to detect suspicious behavior in real-time and test rules on historical data to simulate performance
![Case management](https://static.developer.mastercard.com/content/brighterion/uploads/case_management.svg)

###### Case management {#case-management}

Create and manage cases from alerts, enabling investigators to handle cases end-to-end with user assignment by priority, status, and tasks, while providing reporting capabilities and allowing administrators to manage permissions and user profiles
![Business insight](https://static.developer.mastercard.com/content/brighterion/uploads/business_insight.svg)

###### Business insight {#business-insight}

Create customizable reports across any payment/account portfolio with the ability to define key performance indicators and level of detail based on role and need

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