AI Based Fraud Detection
Bank fraud is an unethical and/or criminal act by an individual or organization to illegally attempt to possess or receive money from a bank or financial institution. Financial organizations around the globe lose approximately 5 percent of annual revenue to fraud, and while direct losses due to fraud are staggering in dollar amounts, the actual cost is much higher in terms of loss of productivity and loss of customer confidence (and possible attrition), not to mention losses due to fraud that goes undetected.
Fraud detection has been one of the major challenges faced by all banks and financial institutions. Such frauds deeply impact bank operations, their capability to grow, and maintain profitability. It also impacts the reputation of the bank for its existing and new customers.
Key Challenges in the Industry
Are you facing frequent fraudulent transactions & theft?
Is your bank facing increasing NPA due to banking frauds?
Are you more Susceptible to frauds with the rise in Digital Banking?
Facing Challenges in decision making due to Petabytes of Transactional data?
Benefits with Fraud Detection Analytics
Minimizing Fraudulent banking thefts to reduce operational losses
Deeper Relationship & Customer Satisfaction
Saving Financial losses due to detection of potential Fraud before they occur
A reliable Banking Reputation towards Market & the Customers
Synctactic AI-based Fraud Detection
Synctactic AI Machine Learning based Fraud detection system involves creating models that have enough intelligence to properly classify transactions as either legit or fraudulent, based on transaction details such as amount, merchant, location, time, and all-important transactional data, behavioral patterns and anomalies.
Neural network-based behavior models for Fraud Detection
Synctactic AI leverages Neural network-based behavior analytics models for Fraud Detection. These models are trained based on various customer profiles, behavioral patterns, and preferences. This enables the banking system to mitigate the potential frauds and its management all over the banking operations. Banks can leverage advances using Synctactic.AI data modeling and data engineering tools like sync discover, sync learn , sync data, sync analyze and their data driven analytics to improve fraud prevention and reduce their fraud losses.
AI-based Anomaly detection for flagging fraudulent transactions
Synctactic AI & Machine Learning models are efficient in Big Data analytics and Data Mining that can go well beyond computer monitoring. The Analytics tool identifies suspicious cases based on patterns that are suggestive of fraud. These patterns fall into categories like Unusual data, Unexplained relationships between otherwise seemingly unrelated cases, Generalizing characteristics of fraudulent cases, and anomaly detection. The AI-based anomaly detection is a Synctactic’s AI technique for identifying deviations from a norm – for automating fraud, cybersecurity, and anti-money laundering processes.
AI-based Real-Time Fraud detection for Proactive alerts
Synctactic AI Data modeling tools and analytics engine facilitates automated analysis of identification and reporting of fraud attempts on time. The analytics platform enables real-time transaction screening, third-party screening as well as compliance solutions. Additionally, Synctactic AI Data Engineering tools provide a visual representation of complex data patterns and outliers to translate multidimensional data into meaningful pictures or graphics so that banks can have a constant watch on flagged potential fraudulent events that might happen in the future.
Data-centric and model-driven Fraud Detection System
While there is no silver bullet for fraud protection, Syntactic AI is constantly striving to develop accurate fraud detection system by taking a data-centric and model-driven approach such as qualitative forecasting, Time series analysis, Causal model using their existing patent-pending data automation tools like Sync discover, Sync learn, Sync data, Sync analyze and bring the true power of AI in client’s systems using their internal and external data. Also, Synctactic AI Fraud analytics combs through data and combines data from multiple sources including public records and integrates it into a model.