AI Based Fraud Detection

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?
With the rapidly growing banking industry in India, frauds in banks are also increasing very fast, and fraudsters have started using innovative methods. These bank frauds are becoming more and more common in the digital platform leading to many fold losses for banks and slowing their growth.
Is your bank facing increasing NPA due to banking frauds?
It is evident that the post-liberalization era has showered new colors of growth upon the Indian banking sector but simultaneously it has also posed some serious challenges; one of them being the rise in frauds leading to more and more NPAs. This unhealthy development of rising fraudulent activities afflicting the banking sector generates not only losses for the banks involved but also impinges their credibility adversely.
Are you more Susceptible to frauds with the rise in Digital Banking?
While technology is scaling the pace of banking operations and taking it to the new territories it is also very evident that banks and financial institutions are increasingly vulnerable to various risks such as phishing, identity theft, card skimming, vishing, messaging/OTP frauds, viruses and Trojans, spyware and adware, social engineering, website cloning and cyber stalking.
Facing Challenges in decision making due to Petabytes of Transactional data?
Banks are overwhelmed with the amount of data they have to deal with. Internet payment companies providing alternatives to traditional money transfer methods must adopt deep learning-based data analytics systems that are much more reliable and capable of identifying complex patterns and characteristics for cybercrime and online fraud detections.

Benefits with Fraud Detection Analytics

01.
Minimizing Fraudulent banking thefts to reduce operational losses
Criminals are smart. They find creative ways around security measures. Proactive detection helps banks notice these trends and react accordingly to improve their security and protect their customers’ information. This enables banks to limit their customers' exposure to fraudulent activities and banking thefts.
02.
Deeper Relationship & Customer Satisfaction
It’s frightening and alienating to open up a credit card statement and see a bunch of charges you didn’t make. It’s much more comforting to have the bank contact you first. Then you know the bank is looking out for you, and you feel more confident that the issue will be resolved correctly. Even if there is no fraud, most customers appreciate a call when there’s unusual activity on their accounts.
03.
Saving Financial losses due to detection of potential Fraud before they occur
Predictive analytics is one of the advanced analytics which is used to make predictions about unknown future events related to banking frauds, card thefts, or NPAs. Predictive analytics uses many techniques from data mining, statistics, modeling, machine learning, and artificial intelligence to analyze current data to make predictions about the potential frauds and banking thefts.
04.
A reliable Banking Reputation towards Market & the Customers
Fraud detection in banking is a vital aspect of any financial institution. Dependency on technology and data analytics is increasing because banks want to predict fraudulent transactions, banking thefts, or possible NPA much before they occur. This is to ensure that they can sustain their market position while minimizing their losses and keeping a formidable great reputation in the market.

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.

To immediately leverage this power of AI on your existing financial and banking systems please connect with one of the data scientist members of the Syntactic.AI team.

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