AI-Powered Fraud Prevention Solution for Banking and Payment Platforms
MultiQoS helps banks, credit unions, and payment platforms prevent losses from financial fraud before settlements take place, reduce false positives, and automate investigation workflows.
Most of the compliance teams spend their days reviewing alerts to determine if they're real, resulting in a large number of legitimate transactions getting flagged as fraudulent. By leveraging our automated fraud investigation workflows, your compliance team will no longer have to review alerts. Instead, teams get reports with complete transaction history, device context, and rationale for the risk score.
Solution Overview
A Financial Fraud Detection Solution for Modern-Day Fraud Patterns
MultiQoS provides a financial fraud detection solution that plugs directly into your transaction processing stack, analyzing behavioral signals, device signatures, and account history. Leveraging AI in financial fraud detection, our systems generate a risk score before the transaction settles. Unlike rules-based solutions, the MultiQoS AI-driven solution is self-learning, adjusting to new types of attacks in your ecosystem.
In terms of what separates this solution from basic monitoring tools, the solution is specifically trained to understand and recognize patterns in financial transaction data. The nuances of synthetic identity fraud, first-party bust-out fraud, authorized push payment fraud, and other unique characteristics of financial transactions and payments cannot be missed using traditional monitoring approaches.
Features
Key Capabilities of Our AI-Based Financial Fraud Detection Solution
We offer AI fraud detection solutions that reduce fraud losses, customer friction events, and workload for your compliance teams.
Transaction Scoring
With our financial fraud detection solution, all your transactions get scored based on behavioral patterns, device fingerprints, and other key information within seconds.
Adaptive Machine Learning Models
Machine learning models continuously update based on your organization's confirmed fraud cases and legitimate transactions, therefore constantly improving their accuracy.
Automated Case Enrichment
Any alert is enriched with detailed transaction information, including account history, dispute details, connected devices, and location information before reaching a case queue.
Synthetic Identity Detection
MultiQoS cross-references user application information against user behavioral patterns detected during the account origination process, identifying synthetic identities that passed through KYC.
Network-Wide Anomaly Detection
Any correlated anomaly in velocity, device overlap, and frequency among users is detected in the network, catching complex fraud scenarios.
Explainable AI Scoring
Risk scores include explainable rationales behind the decision, providing necessary explanations for your compliance department and additional context for decision-making for analysts.
Custom Rule Layering
For organizations that have an existing rule set, we can add rules as an extra layer to our AI models.
Automatic SAR Reporting
Any confirmed fraudulent case will pre-populate SAR forms automatically.
Challenges
How Manual Approaches to Fraud Detection Are Costing Financial Institutions?
Financial institutions using a combination of rules and human investigators suffer from an inherent delay that arises from the disconnect between the rapidity of current attacks and the slow speed of the investigator's workflow. Here are some failure patterns that we observe in our clients' teams.
Manual Investigation Limitations
The rule engine produces thousands of daily alerts, but most of those alerts are false positives, leading to a backlog.
Static Rules Failure
Scammers keep changing their tactics, but fraud rules lag behind in identifying payment fraud and account takeover scams.
Multiple Data Sources Complications
Investigators gathering evidence from the core banking system, card management tool, and CRM must spend more time compiling heterogeneous data.
Excessive Number of False Positives
Wire transfers and payments get flagged for manual review, support desks must address customer complaints and disputes, resulting in churn.
Frauds Across Multiple Accounts
Individual account monitoring allows fraud rings to use multiple accounts to execute an attack. This network approach enables scammers to move funds between linked accounts quickly.
Reporting Backlog
An investigative pipeline backed up with manual review exposes the institution to increasing costs of penalties that are not directly tied to the fraud loss itself.
Your Pain Points
Hidden Cost of Current Fraud Detection Systems
Many financial institutions absorb losses that never appear as a single line item in the expense column. These are the costs associated with inefficiencies inside your manual approach to financial fraud detection.
Low-Value Alert Review
Fraud operations with a team of 50 people reviewing rules-based queues can lead to delays in processing legitimate transactions.
Missed Opportunities
Traditional fraud tools take months for the fraud loss to materialize as a credit charge-off because, by then, your institution has issued a loan that can no longer be recovered.
Blocked Transactions
Every customer transaction incorrectly identified as suspicious leads to a call to your fraud team and increases the risk of losing the relationship.
Fines for Late Filings
As FinCEN SAR reporting requirements have a deadline of 30 days, a backed-up investigative pipeline will make the deadline impossible to meet.
Account Takeovers Go Unnoticed
Any delay between detecting the initial breach and stopping the transfer makes fraud resolution impossible. In this regard, nightly-batching of the transaction score creates a vulnerability in your detection system.
Failure to Identify Scheme Frauds
Scheme fraud requires coordination across multiple linked accounts, but each transaction is individually small and below thresholds.
Key Benefits
Your Organization's Gain From Financial Fraud Detection Solutions
MultiQoS's financial fraud detection solutions intercept attacks in real-time by scoring transactions at authorization rather than detecting them after the fact.
Higher Value Cases Processed by Investigators
Automated fraud investigation workflows filter out low-priority alerts automatically, so human reviewers can focus on 80-100 pre-enriched cases each day. You'll cover far more ground with your current team.
Substantial Reduction in False Positives
The adaptive AI model learns the unique behavior patterns specific to your customers to reduce the number of alerts. This significantly reduces the number of customer support tickets opened because of false positives.
Compliance Built Into Investigative Workflow
SAR pre-population, audit trails, and case documents will be created throughout the investigation process to ensure your organization stays compliant automatically as part of its regular activities.
Scalable Detection
Cloud-native deployment allows the detection capacity to scale automatically during peak transaction volumes.
How It Works
The Mechanics Behind Our Automated Fraud Investigation Workflows
Our AI fraud detection solution first aggregates transaction information, account activity, device telemetry, and behavioral data from your core banking system, card management system, and digital banking channel through API connectors.
Use Cases
Examples of Where Financial Institutions Leverage AI for Fraud Detection
Our AI fraud detection solutions use transaction scoring based on behavioral velocity data and device fingerprinting to detect card-not-present fraud during authorization, before the merchant processes the settlement.
Authorized Push Payment Fraud Detection
Outbound wire and ACH transfers, which are flagged by behavioral models due to deviations from customer behavior, indicate social engineering authorized payments not detectable by legacy rule engines.
Synthetic Identity Detection at Account Opening
Synthetic identity profiles are detected during application processing with cross-referencing of application data with behavioral signals gathered at account origination time.
Account Takeover Fraud Detection
Login signals, session anomalies, and device consistency checks identify instances of credential stuffing, SIM swap fraud, and phishing attacks targeting customer accounts and their credentials before any unauthorized transfer occurs.
Detection of Bust-Out Fraud Rings
Network-level detection identifies fraud rings by looking for correlations between accounts in terms of shared devices, patterns in transactions, and referrals, which cannot be detected when monitoring accounts individually.
First-Party Fraud Investigations
Our solution constructs automated workflows to investigate first-party fraud cases and determine whether the alleged victim of fraud is attempting to commit chargeback fraud by analyzing behavioral baselines and dispute history.
Mule Account Detection
Identification of accounts used for transferring illicit funds, detected by monitoring sudden changes in transaction frequency, geographic anomalies, and links to known fraudulent networks or individuals.
Business Impact / Results
Results Measured From Our AI Fraud Detection Solutions
MultiQoS provides an AI-powered financial fraud detection solution that reduces losses and automates investigation workflows.
False Positive Alert Reduction
Reduction in false positive alerts freeing analyst capacity for real fraud investigation.
Synthetic Identity Detection
Detection rate for synthetic identity fraud stopping fraudulent accounts before they cause losses.
Time to First Production Deployment
From engagement start to live fraud detection in production with zero disruption to existing banking infrastructure.
Fraud-Related Loss Reduction
Reduction in fraud-related losses through real-time AI detection across transactions and identity verification.
Integration
Integrating AI-Based Fraud Detection With Your Existing Technology Stack
No need to replace your entire technology stack. MultiQoS works alongside existing systems through pre-integrated API connectors or an available API layer.
Core Banking Systems Integration
Our financial fraud detection solution integrates with core banking systems via REST and SOAP API connectors, which gather account history and transactions with batch-based ETLs.
Card Management & Payment Networks
Connectors with card management systems and payment network data streams allow us to process all card transactions for scoring immediately after the authorization decision.
Digital Banking Platform Integration
Digital banking platform integration allows our behavioral models to gather device fingerprints, session data, and login behavior, which is crucial for building out behavioral baselines.
Case Management and Compliance
Confirmed fraud cases and SAR data automatically populate fields in your case management platform and allow for continued fraud investigation using the same workflow.
Why Choose Us
Why MultiQoS Is the Right Partner for Your AI-Facilitated Fraud Detection for Finance
The underlying machine learning models used in our AI fraud detection solutions are trained specifically on financial transaction data for banking, credit, and payment use cases. This means it can detect card-not-present fraud, bust-out schemes, and AP/P scams.
Comprehensive Explainable Risk Scoring
Every risk score is provided with an explanation of the contributing signals used.
Smooth Deployment Within Existing Systems
Our API integration layer manages schema drift and limits of the legacy APIs, ensuring deployment without requiring costly core system updates.
Dynamic Calibration of AI Models
Unlike traditional rule-based scoring systems, our machine learning models adapt as your analysts approve and deny cases, providing ongoing refinement in the process.
End-to-End Compliance
Our solution ensures automated regulatory compliance by pre-populating SAR forms, creating comprehensive audit trails, and generating necessary case documentation.
Tie up with MultiQoS to Stop Losing Money From Financial Fraud
Get your MultiQoS system live within 90-120 days, integrate with your stack, and start seeing a drop in alert volume and fraudulent activity by the end of your first quarter.
Connect With Our Experts
FAQs
Frequently Asked Questions
How Does The Accuracy Compare Against Rule-Based Solutions?
Traditional rule-based systems tend to achieve precision rates of 5-15%. MultiQoS-trained models will provide 35-55% precision in the first 90 days, improving with continued tuning and feedback from your analysts. This means far fewer false positives, resulting in fewer false declines and delays for legitimate customers. This is just as important as improved detection in customer-centric organizations.
How Long Will Integration Into Our Core Banking Stack Take?
Typically, initial deployment to handle triage of fraud alerts takes between 90 and 120 days, with an additional 60-90 days allocated for parallel validation. MultiQoS has been successfully implemented into legacy core banking platforms with a dedicated, documented API layer that can accommodate any inconsistencies.
Can I Preserve Current Fraud Rules and Compliance Procedures?
Absolutely. MultiQoS works to complement your existing solution rather than replace it. In other words, organizations that need jurisdiction-specific thresholds, have customized suspicious activity reporting processes, and any type of custom alert workflow can leverage AI-based models for detection while maintaining everything else as-is.
What Do We Do With Blocked Customer Transactions?
The adaptive models are trained on your own transaction history instead of industry-wide averages. So, organizations implementing this technology usually experience 40-60% reductions in alert volume within three months. When some transactions still trigger unnecessary alerts, the comprehensive case enrichment ensures the alert is cleared efficiently. Feedback collected during the clearance process is used to further refine the model.
What Are The Data Privacy And Security Requirements?
PII and financial transaction data managed in the solutions are subject to configurable data residency and encryption controls. The platform supports deployments in the cloud, hybrid, or on-premises, depending on your preferences, GLBA, PCI-DSS, and/or state privacy requirements. All data ingested for machine learning training purposes is handled according to your organization's current controls.
How Much ROI Can I Expect?
Fraud detection ROI is composed of two parts: reductions in fraud-related losses and reductions in operational expenses. Organizations with adaptive machine learning models in place report average losses reduced by 35%. When it comes to savings from operations, AI-based fraud detection solutions allow a 50-person team to investigate as many alerts as a 75-person team. Average payback periods tend to be 12-24 months.