Fighting financial crime with artificial intelligence
SwiftDil pioneers the one-stop solution paradigm for AML / KYC compliance using AI technology
The SaaS market is now growing at an annual rate of 18%, according to the cloud computing market by service, deployment model, organization size, vertical and region Global Forecasts 2025. By the end of 2021, 99% of companies will have implemented one or more SaaS solutions. Almost 78% of small businesses have already made a SaaS investment. The use of SaaS in the healthcare industry is increasing by 20% every year. Another statistic revealed that remote identity verification is expected to reach $ 16.7 billion by 2028.
According to a recent analysis by McKinsey & Company, technology industry analysts estimate that the software as a service market is expected to grow even more in the coming years, with the market for SaaS products expected to reach around $ 200 billion dollars. by 2024. The growth of the industry may be linked to the success of companies such as global identity provider ComplyCube, Berlin-based company Passbase, and London-based fintechs such as SwiftDil and Onfido. Meanwhile, the high value of the tech industry has made it very attractive for financial fouls, sparking a tech arms race between scammers and the companies mentioned above.
Exposing financial crimes
The 21st century, a digital and truly converging era in which disruption is increasingly common, has seen a shift not only in the nature of financial crime, but also in its frequency and complexity. Financial crime, a $ 2.1 trillion problem, is undoubtedly one of the largest and most profitable industries in the world. The latest controversy over the âRussian laundryâ illustrates the scale of the problem. This scam resulted in the transfer of $ 20 billion from Russia to 732 banks in 96 countries, including Hong Kong and China. He not only demonstrated the transnational dimension of financial crime, but also the scale of the problem facing the stewards of the global financial system. As a result, enforcement measures have been strengthened, with renewed emphasis on stopping the flow of illicit funds.
KYC and AML in business
To prevent fraud, money laundering and other financial crimes, KYC and AML compliance is essential. If you allow consumers to transfer money, you could be a target for money laundering regardless of your industry. An effective compliance program ensures that you and your customers can do business with confidence, whether you are a bank, fintech, or marketplace. Artificial intelligence is playing an increasingly important role in the economy, but how does it help financial institutions meet their compliance obligations? Artificial Intelligence (AI) advances KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance. AI is more than technology; it is a set of connected technologies with the ability to automate workflows and quickly assess huge amounts of data of various forms.
Anti-money laundering and business solutions
The global market for anti-money laundering solutions is growing at a rapid pace in this technological age. Numerous studies have been conducted on this topic, and the global AML solutions market is predicted to grow from $ 2.2 billion in 2020 to $ 4.5 billion in 2025, with an YBBO of 15.6%.
Businesses can benefit significantly from facial biometric technologies such as alertness detection systems throughout the onboarding process. The use of computer vision and deep learning algorithms to detect “vividness” or “presence” in a person is an essential part of any biometric identification solution, which goes far beyond the more commonly understood concept of facial verification. Passive liveliness detection technology works in the background without the user being aware of it, a technique known as “security through the dark.” It can tell the difference between a living person’s face and an inanimate or rigged face by detecting presentation attack elements such as edges, texture, and depth. It also cannot be fooled by animation software that reproduces facial expressions like smiling or frowning. Deepfakes, masks, dolls, and other attack vectors can all be treated.
Another solution against financial crimes is the 2 + 2 verification. There are several sources of correspondence that can help ensure that potential buyers are generally genuine and not part of a scam or an international terrorist organization. This is called â2 + 2â identity verification, which can involve up to four different methods to confirm the identification of a person. Instead of looking at a single credit report, a business could check multiple sources to make sure the name, address, and date of birth (for example) match at least two or more additional sources.
Fighting financial crime: the rise of SaaS AML
To combat increasingly sophisticated fraud, several AML / KYC SaaS platforms have emerged with various approaches; some focus on customer onboarding, others focus on remote identity verification or biometric solutions. Nonetheless, a number of AML / KYC vendors have embraced the one-stop-shop solution paradigm pioneered by the London-based company. SwiftDil. Previously, companies had to integrate with multiple vendors to meet their AML / KYC needs, while most vendors offered limited-feature platforms and APIs with opaque pricing. One-stop-shop solutions aim to meet business AML / KYC needs through a single API or SaaS offering, typically encompassing AML checks, customer screening, and biometric verification. SwiftDil, which revolutionized the identity verification AML / KYC compliance market in 2016 with feature-rich, AI-based Software as a Service (SaaS), continues to offer one of the SaaS platforms the most complete and innovative on the market. It recently upgraded its SaaS offering with advanced vividness detection technology and 2 + 2 verification in multiple countries.
It now has clients around the world in financial technology, legal, telecommunications, financial services, recruiting, insurance, healthcare, e-commerce, cryptocurrency, travel, saving concerts and more.
The future of financial security
Machine learning begins to migrate to the cloud as a massive amount of data becomes more readily available. Data scientists will no longer be responsible for specialized coding or infrastructure management. AI and machine learning will allow systems to adapt to them, create new models on the fly, and deliver more accurate and timely results.