The complexities of state sponsored terrorism, professional criminals and evil underground actors are becoming increasingly difficult to understand, track, expose and prevent. In today’s world, fraud detection requires a holistic approach that matches data points with activities to determine what is abnormal.
Global tech giant unveiled details of its upcoming AI-powered chip designed to bring deep learning inference to enterprise workloads to help fight fraud in real time, identify and stop a variety of fraudulent attacks and crimes quickly and accurately – while improving customer and citizen experiences.
The AI chip includes on-chip acceleration for AI inference during a transaction. The breakthrough of the new on-chip hardware acceleration, which took three years to develop, is intended to help customers gain large-scale business insights in banking, finance, business, insurance applications and interactions with customers.
According to a recent study commissioned by the tech company, 90% of those surveyed said it was important to be able to create and run AI projects, regardless of where their data is located. According to the company, the technology is intended to enable applications to run efficiently regardless of the location of the data, helping to overcome traditional approaches to enterprise AI that tend to require significant capabilities to run. memory and data movement to handle inference.
Businesses in the Philippines need this technological support; in 2017, an inside fraud involving the theft of 1.75 billion pesos ($ 34.5 million) from a major Filipino lender was exposed, in the latest controversy affecting the Filipino banking sector. According to reports, the bank discovered the fraud after a customer denied taking out the loans. During the 2019 coronavirus disease (Covid-19) pandemic, there has been an increase in digital fraud attempts against businesses and consumers in the Philippines.
“With this technology, the accelerator close to mission-critical data and applications means that businesses can perform high-volume inference for sensitive transactions in real time without the need for off-platform AI solutions, which can have an impact on performance. Customers can also build and train off-platform AI models, deploy and infer an analytics system, ”the company said.
According to the company, companies typically use detection techniques to detect fraud after it has occurred, a process that can be time consuming and computationally intensive due to the limitations of current technology, especially when fraud analysis and detection is carried out away from the mission. -transactions and critical data.
“Due to latency requirements, complex fraud detection often cannot be done in real time – meaning a malicious actor might have already successfully purchased goods with a stolen credit card before the retailer know that a fraud has taken place, “he said.
The new chip has an innovative centralized design that allows customers to use the full power of the AI processor for specific AI workloads, making it ideal for financial services workloads such as fraud detection, loan processing, transaction clearing and settlement, anti-money laundering. , and risk analysis. The chip features 8 processor cores with an out-of-order, deep superscalar instruction pipeline and a clock rate of over 5 GHz, optimized for the demands of heterogeneous enterprise-class workloads.
“With these innovations, customers will be able to improve fraud detection based on existing rules or use machine learning, speed up credit approval processes, improve customer service and profitability, identify transactions or transactions that are likely to fail and propose solutions to create a more efficient settlement. process, ”the tech company said.
The detection and prevention of fraud is not a one-time event. There is no beginning or end. Rather, it is a continuous cycle of monitoring, detection, decisions, case management and learning that feeds detection improvements back into the system. Organizations should strive to continuously learn from fraud incidents and incorporate the results into future monitoring and detection processes. As artificial intelligence and machine learning become more mainstream, the next generation of technology is automating the manual processes associated with combining large data sets and using behavioral analysis.