One subject never fails to light up the eyes of senior bankers and regulators when they're questioned about their efforts to end the money laundering-related scandals that have spread across northern Europe over the last two years: technology. There can be no more damning indictment of the integrity of a bank, or its host nation, than the public revelation that a licensed institution is being used as a laundromat for ill-gotten gains. And what is more enlivening for money-laundering supervisors and bank-compliance officers than showing your firm and country is at the forefront of a technology that could make these troubles disappear? Some of the biggest actors in Europe's financial sector are converts. The UK's Financial Conduct Authority is particularly enthusiastic about using technology to fight money laundering.

In 1981, Bill Gates said "640KB will be enough for anybody". Surpisingly, it was hard even then, to imagine where we would be today; in a sea of data. Big data is fuelling the world of business, driving human decision making and building machine learning systems which have become essential components to success in the growing sector of digital banking, particularly in Monzo and Revolut. Big Data, as the name suggests, provides 1000's of metrics which inform many real decisions, so how has Data ensured the continuing success at these innovative companies? Monzo, founded in 2015 by 5 young men who had previously worked for Starling Bank provided a truly unique banking experience than any on the market at the time.

The Financial Crime Technology department is at the forefront of Revolut's efforts to keep customers and their money safe. Through the intelligent development and deployment of technology, we aim to prevent financial crime more effectively than traditional banks.We operate in small, nimble teams that are built for scale, speed, and quality. Each unit is responsible for a different pillar of fincrime, giving you the chance to have a real impact on crucial problems that affect millions of people around the world. Our Machine Learning Engineers are responsible for developing and operationalizing online and offline algorithms which integrate with Revolut's server layer. They are skilled in software engineering, machine learning, network science and applied mathematics.

App-only fintech startup Monzo is scaling up its machine learning capabilities and plugging employees into the rich stream of data generated by 1.7m global users as it forges ahead of the high-street banks in the digital space. Neal Lathia, data scientist and machine learning lead at the UK-based bank, told The Drum how his team is delivering "incremental gains" in the app, in customer and the product using smarter automation since he took the role on one year ago. Lathia, previously lead data science at SkyScanner, commands a team of more than 20 people. When he joined there were only two. Now he is looking to harness the wealth of data at the company's disposal to get Monzo "working faster". It contrasts with the role a data scientist at a high street bank may have, weighed down by legacy systems, corporate baggage and sheer scale.

SME payments are "broken" and the industry's reliance on old-world processes "staggering", according to Paul Christensen, CEO and founder of machine learning firm Previse. "If I walk into Starbucks and ask for a coffee. I get my coffee and Starbucks gets paid instantly," says Christensen. "If this were a B2B scenario I would walk in, ask for a coffee and then send an invoice in three months. That sounds ridiculous, and yet that is how the entire world of B2B transactions work."

The Singapore Fintech Festival was probably the biggest one here yet, and the Monetary Authority of Singapore (MAS) has made some definitive pushes into big data, artificial intelligence, and the launch of an ASEAN-wide API to answer the global demand for open banking principles. More recently though, MAS has given an idea of their plans for the future, with a US$5 billion (SG$6.91 billion) worth private market programme allocated as its investment portfolio into the private markets asset class--a move by MAS to anchor global asset managers into Singapore and catalyse more growth in that sector of Singapore. MAS has also announced a "Sandbox Express" to complement its existing sandbox, which are pre-defined sandboxes so that fintechs can conduct their experiments more quickly than the existing measures. And all of this does not yet venture into the often volatile, but always interesting startup sector. Fintechnews has identified 13 promising Singapore based Fintech companies which will shake 2019's direction and purpose, one way or another.

In April 2018, FinTech startups across the globe raised $2.18 billion worth of VC investments across 107 deals. In terms of MoM growth, in April 2018, global FinTech funding witnessed a significant decline of 37.5% from the $3.49 billion raised in March 2018. The US continued to dominate the funding charts with $883.5 million, which was 41% of the global FinTech funding (by value) in April. Hong Kong comes second with a 29% ($625 million) contribution, with UK, India, and China contributing 14.4% ($300 million), 4% ($96.8 million), and 3.6% ($79 million) respectively. AI/ML segment led the funding race in March 2018 with $613 million in funding across only two deals.

UK-based payments start-up Revolut has unveiled its "human-like" chatbot, Rita, to answer customer queries. Rita – which stands for "Revolut's Intelligent Troubleshooting Assistant" – is accessible via the mobile app's messenger support service. Customers can open the app, send Rita a message and get an instant reply. During the pilot scheme, Rita resolved 20% of 7,500 customer queries per day and received a customer satisfaction score of 4.3/5. Did Rita get asked out on any dates?