In recent years, deep learning techniques have proved to be highly valuable for tackling countless research and real-world problems. Researchers at Feedzai, a financial data science company based in Portugal, have demonstrated the potential of deep learning for the prevention and detection of illicit money laundering activities. In a paper presented at the 3rd ACM International Conference on AI in Finance, the team at Feedzai introduced LaundroGraph, a self-supervised model that could simplify the cumbersome process of reviewing large amounts of financial interactions looking for suspicious transactions or monetary exchanges. Their model is based on a graph neural network, an artificial neural network (ANN) designed to autonomously process large amounts of data that can be represented as a graph. "Wanting to strengthen our AML solution, and after identifying major pains with the current AML reviewing process, we thought about solutions to overcome these challenges using AI," Mario Cardoso, a Research Data Scientist at Feedzai, told TechXplore.

Artificial intelligence is the new electricity -- it's rapidly transforming every industry, enhancing our lives, and creating huge economic value. However, left unchecked, AI also has the potential to inflict harm upon humanity. We have seen cases where it has discriminated against minority groups and unintentionally promoted hateful and violence-inciting speech. How can we ensure that AI remains a force for positive change while reducing its capacity to cause harm? To address this complex problem, we joined forces with nine other startups (including global industry-leaders like Feedzai and Talkdesk) and six AI research centers to form the Center for Responsible AI.

An unprecedented volume of data is currently being generated across the globe with no less than an estimated 2.5 quintillion (1018) bytes of data each day at our current pace. The variety of formats in which this data is being produced, and its structural complexity, are also on the rise. Collectively, these factors are driving demand among institutions for advanced analytics to generate actionable insights. At the most basic level, machine learning encompasses the use of computational algorithms more advanced than the analytics methods (data mining approaches, for example) traditionally employed to deliver insights into large datasets. Machine learning techniques are firmly rooted in the science of statistics and have valuable applications not least in financial services.

Current anti-money laundering solutions rely on techniques that generate excessive false positive rates which require burdensome manual reviews. Legacy money laundering solutions cannot keep pace with the increasingly sophisticated layering schemes, as well as growing compliance requirements from regulators. Can your bank/financial institution successfully navigate the growing regulatory demands? Is your bank equipped with the newest digital and AI based tools to stay ahead of the new global trends in money laundering?

The fight against financial crime is changing and banks are struggling to keep up. Financial institutions are already losing ground in the adoption of open banking initiatives like PSD2. Coupled with the increasing market demands for compliance and transparency brought on by regulations like the GDPR, it's clear that banks have a lot to deal with. The financial industry is quickly shifting towards real-time payments and instant services, two key aspects of a frictionless customer experience. However, these frameworks present serious challenges to the security side of things – particularly where financial crime is concerned.

Manish Kohli, Global Head of Payments and Receivables, Citi's Treasury and Trade Solutions (Citi TTS), has stated that this strategic partnership is a good example of their commitment to using new technologies to drive innovation. In reference to this partnership, Kohli said, "With the help of Feedzai's solution, we can scale rapidly in an effort to deliver value to our clients, allowing them to make payments securely, efficiently and without friction, across the globe." Citi may appear focused on providing risk management without slowing down their claims processing tech stack. In addition to the constant innovations of fraud detection and payment processing, cyber-attacks and other fraud methods are constantly improving. It is because of this that Citi's clients expect progressively faster and safer transactions.

It takes 300 milliseconds for the human eye to blink. It takes 13 milliseconds for the brain to process visual information. In less than 3 milliseconds, Feedzai can evaluate thousands of decisions to score a transaction in real-time. Diana is a data scientist who trains and evaluates machine learning models using Feedzai's platform. The models she builds score the risk of a transaction and automatically identifies financial crime--all in real-time.

Artificial Intelligence (AI) is evolving quickly as the go-getter technology for companies across the world to redefine their services and offerings. The technology itself is inching to become better and smarter day by day, giving high adoption goals to newer industries. There is huge interest garnered when one talks about AI in banking and other financial sectors, a domain which is showing very high adoption rates. The rudimentary applications into AI include introducing smarter chatbots for customer service, placing an AI robot for self-service at banks and personalising services for individuals. AI enables the Banks to bring in more efficiency to their back-office in a bid to reduce fraud and security risks.

The fight against financial crime is changing and banks are struggling to keep up. Financial institutions are already losing ground in the adoption of open banking initiatives like PSD2. Coupled with the increasing market demands for compliance and transparency brought on by regulations like the GDPR, it's clear that banks have a lot to deal with. The financial industry is quickly shifting towards real-time payments and instant services, two key aspects of a frictionless customer experience. However, these frameworks present serious challenges to the security side of things – particularly where financial crime is concerned.

This is a preview of the AI in Banking and Payments (2018) research report from Business Insider Intelligence. To learn more about the use cases, trends and future of AI in finance, click here. Current subscribers can read the report here. Artificial intelligence (AI) is one of the most commonly referenced terms by financial institutions (FIs) and payments firms when describing their vision for the future of financial services. AI can be applied in almost every area of financial services, but the combination of its potential and complexity has made AI a buzzword, and led to its inclusion in many descriptions of new software, solutions, and systems.