Financial Anomaly
Create value and proactively mitigate financial risk by unleashing the power of AI anomaly detection on financial data and records
Financial Anomaly Detector
AIFAD the AI Financial Anomaly Detector uses specialized AI algorithms to model the behavior of agents, transactions and processes in financial systems, and detect anomalies, errors, fraud, waste, manipulations and collusions.
Advanced AI Behavior & Anomaly Modeling Ecosystem
- AI Edge systems are industry, machine, system and operation agnostic. They are solely data driven, and consume clients side data to build and train custom AI models for their operations.
- Our API friendly ecosystem offers a powerful data platform which automated the integration, formatting and processing of big data to deep data and intelligent features for AI processing.
Features
Features of AIFAD the AI Financial Anomaly Detector
- Financial anomalies are complex and can’t often be detected in isolation. They can rather be identified in lateral shifts when considered across several categories, levels or stages.
- A second layer meta-learner AI implements scoring that assesses and ranks financial anomalies by different risk metrics, enabling focused attention on high-risk cases.
- Unlike preventive maintenance where we have full catalogs of anomalies in the event logs of each machine, a financial anomaly is an exceptional isolated event. We employ specialized unsupervised AI models that can learn from and adapt to any financial ecosystem and identify previously unseen anomalies.
- Root cause identification and forensic analysis is enabled through detailed reports to deepen your understanding of the anomalies detected.
- In back-testing & scenario mode, the AI model can be executed on financial data with the addition of what-if-scenarios to measure the effectiveness of the AI anomaly detection model, and to enhance your ability to prevent future cases of anomaly, fraud or errors.
- Set custom alert thresholds for different groups, accounts, transactions or activities, ensuring relevant notifications are issued to avoid information overload.
Benefits
Benefits of AIFAD the AI Financial Anomaly Detector
Insider trading & market manipulations
Pint point insider trading, collusion and market manipulation for energy and financial market operators and regulators.
Banking Fraud
Enable banks, financial institutions, exchange operators and corporate treasuries to detect insider fraud, theft and data manipulation. Uncover fraudulent payment solutions and credit card use.
Corporate Fraud
Identify fraudulent financial statements and tax filings, as decision making support for investors, financial, credit and bond market analysts, regulators and tax authorities.
Real-time Accounting & Audit
Transform accounting and financial auditing from quarterly to real-time processing by identifying erroneous data entries, improving accuracy, enforcing continuous accounts and transactions monitoring and optimizing financial planning.
Insurance Fraud
Identify erroneous or fraudulent insurance claims from both clients and service providers.
From Financial Big Data to Deep Data, We Accelerate Transition to Intelligent Digital Enterprise
Sys Edge Data Hub is a powerful datahub that can be configured to fetch, organize, structure and manage complex financial data types from multiple systems and in different formats.
Financial anomaly detection needs a vast array of raw data from assortments of transaction, financial and treasury systems within multiple ecosystems. These data are not directly suitable for behavior and anomaly modeling. They have disparate timestamps, formats, and types. They are too large in size, lack indexing and structuring for feature extraction. They require processing and transformation to ensure efficient handling. Sys Edge Datahub is a flexible API friendly data platform specially designed to fetch, gather, format, structure, transform and manage financial data.
AIFAD the AI Financial Anomaly Detector has built-in data processing & feature extracting toolboxes that apply a range of specialized mathematical transformations to quantify, enhance with contextualization and extract behavioral features from financial and transaction data suitable for AI anomaly detection.
- The configuration of a custom financial anomaly detection model for a company begins with a custom financial feature extraction model in formats that are suitable for actionable AI modeling according to groupings aligned with an organization's specific transaction management, financial and accounting objectives.
- Custom feature contextualization and enhancement adds valuable nuances and insights into users and transactions behavioral patterns.
- Feature mapping creates features across users and entities: point features are quantified per single entity, peer grouping features are quantified across a group of peers in the same category, horizontal features are quantified across multiple categories.
