SwiftDil is pioneering the one-stop solution paradigm for AML/KYC compliance using AI technology
The SaaS market is now increasing at an annual rate of 18%, according to the Cloud Computing Market by Service, Deployment Model, Organization Size, Vertical And Region 2025 Global Forecast. By the end of 2021, 99% of businesses will have implemented one or more SaaS solutions. Almost 78% of small businesses have previously made a SaaS investment. The usage of SaaS in the healthcare industry is increasing at a rate of 20% each year. Another statistics have revealed that remote identity verification is expected to reach $16.7 billion by 2028.
According to a recent McKinsey & Company analysis, technology industry analysts estimate the software as a service market to grow even more in the coming years, with the market for SaaS products expected to reach about $200 billion by 2024. The industry growth can be linked with the success of companies such as the global identity provider ComplyCube, the berlin-founded Passbase, and the London-based Fintechs such as SwiftDil and Onfido. Meanwhile, the extensive value of the technology industry has made it quite appealing for financial misconduct, triggering a technological arms race between fraudsters and the companies mentioned above.
Exposing Financial Crimes
The twenty-first century, a digital and truly converged era in which disruption is becoming more common, has seen a shift not only in the nature of financial crime, but also in the frequency and complexity of it. Financial crime, a $2.1 trillion problem, is undoubtedly one of the world’s largest and most profitable industries. The latest “Russian Laundromat” controversy exemplifies the scope 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. It not only demonstrated the transnational dimension of financial crime, but also the magnitude of the issue facing global financial system stewards. As a result, enforcement measures have been increased, with a renewed emphasis on halting the flow of illicit funds.
KYC And AML In Business
For fraud, money laundering, and other financial crimes to be avoided, KYC and AML compliance are essential. If you allow consumers to move money, you could be a target for money laundering, regardless of your sector. An efficient compliance program ensures that you and your clients can do business with confidence, whether you’re a bank, fintech, or marketplace. Artificial intelligence is playing an increasingly important role in the economy, but how is it assisting financial institutions in meeting their compliance obligations? Artificial Intelligence (AI) advances KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance. AI is more than a technology; it’s a collection of connected technologies with the ability to automate workflows and quickly evaluate enormous amounts of data of various forms.
Anti-Money Laundering & Business Solutions
The global Anti-Money Laundering solutions market is growing at a rapid pace in this technological age. Many studies have been conducted on this topic, and it is expected that the Global AML Solutions Market would expand from USD 2.2 billion in 2020 to USD 4.5 billion in 2025, with a YBBO of 15.6 percent.
Companies can benefit substantially from facial biometric technologies such as liveness detection systems throughout the onboarding process. The use of computer vision and deep learning algorithms to detect “liveness” or “presence” in a person is a critical component of any biometric identification solution, which goes far beyond the more popularly understood concept of facial verification. Passive liveness detection technology operates in the background without the user being aware of it, a technique known as “security via obscurity.” It can tell the difference between a live person’s face and an inanimate or faked face by detecting presentation attack elements like edges, texture, and depth. It can’t be fooled by animation software that replicates facial expressions like smiling or frowning, either. Deepfakes, masks, dolls, and other attack vectors can all be dealt with.
Another solution against financial crimes is 2+2 verification. Multiple matching sources can help ensure that potential buyers are generally genuine and not part of a con or international terrorist organization. This is known as “2+2” identity verification, which can involve up to four different methods of confirming a person’s identification. Instead of reviewing one credit file, a company could check many sources to ensure that the name, address, and date of birth (for example) match on at least two or more extra sources.
Tackling FinCrime: the rise of AML SaaS
To tackle the 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. Nevertheless, a number of AML/KYC vendors have adopted the one-stop solution paradigm pioneered by the London-based SwiftDil. Prior to that, businesses had to integrate with multiple providers to meet their AML/KYC needs when most providers offered feature-limited platforms and API with opaque pricing. One-one stop solutions aim to meet the AML/KYC needs of the business through a single API or SaaS offering, typically encompassing AML checks, customer screening, and biometric verification. SwiftDil, who disrupted the identity verification AML/KYC compliance market in 2016 with an AI-driven and feature-rich Software as a Service (SaaS), continues to offer one of the most complete and innovative SaaS platforms in the market. It has recently enhanced its Saas offering with advanced liveness detection technology and 2+2 verification across several countries.
It now boasts customers around the globe in FinTech, legal, telecoms, financial services, recruitment, insurance, healthcare, e-commerce, cryptocurrency, travel, gig economy and more.
Future of Financial Security
Machine Learning is beginning 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 enable systems to scale for them, build new models on the go, and give more accurate and timely results.