Intel has launched the Intel Saffron Anti-Money Laundering (AML) Advisor aimed at detecting financial crime through a transparent artificial intelligence (AI) solution using associative memory.
The launch kicks off the first associative memory AI solution specifically tailored to the needs of financial services institutions and is optimised on Intel Xeon Scalable processors.
The AML Advisor surfaces these patterns in a transparent way, paving the way for “white box AI” in enterprise applications. These solutions are designed to enhance decision-making in highly complex tasks, and early results indicate they can catch money launderers with unprecedented speed and efficiency.
Financial crime is at all-time highs. According to the United Nations, the estimated amount of money laundered globally in one year is 2% to 5% of global GDP, or $800-billion to $2-trillion. In addition, in 2016 alone, approximately 15,4-million consumers were victims of identity theft or fraud, resulting in $16-billion in losses.
“Intel Saffron’s mission is to minimize the time and effort it takes to reach confident decisions,” says Gayle Sheppard, vice-president and GM of Saffron AI Group at Intel. “We accelerate the path to decision by surfacing and explaining patterns in data with speed, precision and accuracy.
“The amount of data that banks and insurers collect is growing at massive scale, doubling every two years. While the quantity of data is growing, so are the types and sources of data, which means today much of the data isn’t queried for insights because it’s simply not accessible with traditional tools at scale.
“Investigators and analysts will depend on transparent AI solutions to meet the ever-growing demands of consistency and efficiency from a business, regulatory and compliance perspective.”
Banks and financial organisations often have 50 or more applications that require use of the same personal financial data. Banks want a more efficient way to manage their data, putting an end to moving and replicating data, which is costly and increases risk. They also want visibility to the unified knowledge across multiple data sources to better serve customers.
Intel Saffron AML Advisor uses associative memory AI to discover new insights for growing businesses, meeting compliance and regulatory requirements, and fighting financial crime with a suite of features, including:
* Knowledge index: Unifying structured and unstructured data linked into a 360-degree view at the individual entity level, to make sense of the patterns found across boundaries wherever the data is stored. This derives knowledge that is hard to gain with vendor and database proliferation of point solutions.
* Continuous learning: Unlike traditional machine learning methods, Intel Saffron AML Advisor doesn’t require domain-specific models nor training and retraining, resulting in improving the time to insight. The financial services industry faces the challenge of “What will be important tomorrow?” In this dynamic landscape, actionable insights realized in hours or days rather than weeks or months is an imperative.
* Work augmentation: Intel Saffron AML Advisor reduces the human cognitive burden through automation thought processes that work with and for the investigators allowing them to focus on higher value activities.
* Compliance validation: Banks collect the data necessary to comply with various regulations, but often must pay non-compliance fines in the billions due to human error or missed deadlines. Intel Saffron AML Advisor explains the rationale behind the recommendations to help banks meet compliance, mitigate fines and reduce countless hours reworking reports.
Intel also introduced the Intel Saffron Early Adopter Program (EAP), and Bank of New Zealand (BNZ) has joined up.