AI Fraud Detection
Improve fraud detection accuracy and responsiveness with AI
AI-powered fraud detection can improve the timeliness and accuracy of identifying and flagging fraudulent/anti-money laundering (AML) financial transactions. With inadequate or nonexistent fraud identification, you could be costing your company in cash flow, wasted employee efficiency, and brand reputation.
What is fraud detection?
Fraud detection harnesses the power of machine learning and natural language processing technology to identify potentially fraudulent transactions from among the many thousands of transactions that get processed each day.
Fraud detection in fintech startups
The key strategic challenge for small financial firms is ensuring that they do not become overwhelmed with fraudulent transactions that threaten their financial viability.
Fraud minimization in insurance claim processing
Insurance fraud includes a wide range of transaction types, including everything from staged accidents and inflated damage claims to coverage backdating and detail falsification.
Credit/debit card fraud detection
Card-issuing banks and credit unions suffer immense amounts of fraud each year, including bogus purchases, personal information/identity theft, and erroneous payment processing.
How does AI enable fraud detection?
AI fraud detection and mitigation—whether in credit/debit card use or any other financial transaction—is a critical contributor to business success, particularly in industries characterized by high volumes of repetitive transaction types (payments, purchases, identification checks, etc.). Not only can AI-based fraud detection provide the accuracy your company needs, an AI-driven solution is also fully autonomous, continuously evaluating and learning your day-to-day transactions and allowing you to focus on running your business.
An important overarching challenge, regardless of whether you run a bank, credit union, insurance company, or other financial institution, is to detect and stop fraud in all of your financial offerings. The speed, automation, and accuracy delivered by AI-based fraud detection solutions offer early adopters a significant competitive advantage.
Protect customer credibility
Generating repeat business—particularly in the financial world—depends on a firm’s credibility and brand reputation. Failing to catch potential fraudulent transactions will jeopardize the company-customer relationship, ultimately leading to customer churn and reduced market share. In addition, obtaining new customers is far more expensive than retaining existing ones, meaning the penalty for churn is increased cost.
Automate fraud detection to unburden your staff
Identifying and dealing with fraudulent transactions burdens staff with countless hours each year. Automating this arduous task with AI fraud detection frees up your team to work on higher-value activities, increasing a company’s bottom line and increasing employee satisfaction and career development.
Increase cash flow
Even if successfully detected and resolved, fraud still costs a company in lost productive hours and reputational damage. More important, though, are fraudulent transactions that go undiscovered until significant sums have been lost to cybercriminals or other bad actors.
AI fraud detection solutions across industries
AI-powered fraud detection in banking
Protect yourself from lost revenue, reduced staff efficiency, and reputational damage with AI fraud detection that provides card-issuing banks and credit unions with automated transaction checking and anomaly identification.
- Eliminate personal information compromises and identity theft.
- Ensure compliance with PII regulations.
- Avoid lost revenue from fraudulent transactions.
AI-powered fraud detection in insurance
Identify and eliminate insurance fraud, e.g., fake accidents, inflated damage claims, and coverage backdating, with AI fraud detection that uses machine learning and natural language processing to proactively and automatically identify processing errors and bogus claims.
- Reduce lost income from bad claims.
- Increase staff efficiency in handling fraudulent transactions.
- Safeguard company reputation and credibility.
AI-powered fraud detection in fintech
Use AI fraud detection to minimize the loss risks associated with moving traditional financial services onto new high-technology platforms.
- Increase ROI of new high-tech financial systems.
- Reduce the risk associated with roll-out of leading-edge financial products.
- Increase confidence of customers with new financial offerings.
How SparkCognition delivers fraud detection
SparkCognition AI fraud detection solutions use our automated machine learning algorithms to analyze your existing historical and real-time financial transaction data to scan for and flag potentially fraudulent items, all while minimizing false positives and freeing up your staff for other value-added activities.
Implementing our AI fraud detection methodology is straightforward and effective, led throughout by SparkCognition AI and domain experts, with minimal time requirements from your staff.
Step 1: Ingest transaction data
To ingest and analyze your time-series financial transaction data (i.e. previous transactions, audit transactions, logins, contracts, etc.), SparkCognition employs a number of powerful ML techniques. These are effective at automatically handling large training sets with an extensive number of features. The developed models are used to identify and flag potentially fraudulent transactions in production.
Step 2: Use natural language processing to examine written documents
Use SparkCognition’s proprietary natural language processing technology to understand, extract, and analyze features and information from natural language sources, such as call logs/transcripts, emails, and contracts. This allows the models to identify and flag fraud even more rigorously by working from a wider set of features.
Step 3: Scan and flag transactions
Scan every transaction to identify and flag anything that looks potentially fraudulent using anomaly detection for anti-money-laundering (AML)-related, or otherwise suspicious for subsequent verification by staff.