Fighting Financial Crime With Artificial Intelligence
According to the Cloud Computing Industry by Service, Deployment Model, Organization Size, Vertical And Region 2025 Global Forecast, the SaaS market is now growing at an annual pace of 18%. 99 percent of firms will have implemented one or more SaaS solutions by the end of 2021. Almost 78 percent of small firms have already invested in SaaS. SaaS use in the healthcare industry is growing at a 20 percent annual rate. According to other data, the remote identity verification market is anticipated to reach $16.7 billion by 2028.
Technology industry analysts expect the software as a service market to grow even faster in the future years, according to a recent McKinsey & Company report, with the market for SaaS products expected to reach around $200 billion by 2024. Companies like ComplyCube, a worldwide identification provider, Passbase, a Berlin-based Fintech, and SwiftDil and Onfido, all based in London, have contributed to the industry’s growth. Meanwhile, the technology industry’s high worth has made it an attractive target for financial fraud, sparking a technological arms race between fraudsters and the corporations described above.
Financial Crimes Exposure
Financial crime has changed not only in nature, but also in frequency and complexity, in the twenty-first century, a digital and totally merged period in which disruption is becoming more widespread. Financial crime is one of the world’s greatest and most profitable sectors, with a $2.1 trillion annual revenue. The most recent “Russian Laundromat” incident demonstrates the problem’s breadth. A total of $20 billion was transferred from Russia to 732 institutions in 96 countries, including Hong Kong and China, as a result of this fraud. It revealed not just the worldwide nature of financial crime, but also the scale of the problem confronting global financial system stewards. As a result, enforcement actions have been stepped up, with a renewed focus on stopping the flow of illicit funds.
In the Business World, KYC And AML
KYC and AML compliance are critical for avoiding fraud, money laundering, and other financial crimes. Regardless of your industry, allowing customers to move money could make you a target for money laundering. Whether you’re a bank, fintech, or marketplace, an effective compliance programme guarantees that you and your clients can do business with trust. Artificial intelligence is becoming more prevalent in the economy, but how does it help financial institutions satisfy their compliance obligations? KYC (Know Your Customer) and AML (Anti-Money Laundering) compliance are aided by artificial intelligence (AI). AI is more than a technology; it’s a collection of interconnected technologies that can automate operations and quickly analyse massive volumes of data in a variety of formats.
Business & Anti-Money Laundering Solutions
In this technology age, the global market for anti-money laundering solutions is rapidly expanding. Many research have been done on this topic, and the Global AML Solutions Market is predicted to grow from USD 2.2 billion in 2020 to USD 4.5 billion in 2025, with a YBBO of 15.6 percent.
During the onboarding process, companies can greatly benefit from facial biometric technologies such as liveness detecting systems. Any biometric identification solution that goes beyond the more commonly understood concept of facial verification must use computer vision and deep learning techniques to detect “liveness” or “presence” in a person. Passive liveness detection technology works in the background, unnoticed by the user, a technique known as “security via obscurity.” By identifying presentation assault features like edges, texture, and depth, it can tell the difference between a live person’s face and an inanimate or manufactured face. Animation software that mimics facial expressions like smiling or frowning won’t deceive it either. Deepfakes, masks, dolls, and other attack vectors are all combatable.
2+2 verification is another method of preventing financial crimes. Multiple matching sources can help ensure that potential buyers are neither con artists or members of an international terrorist organisation. This is referred to as “2+2” identity verification, and it can include up to four different means of validating a person’s identity. Rather than evaluating a single credit file, a corporation could verify multiple sources to guarantee that the name, address, and date of birth (for example) match on at least two or more additional sources.
The rise of AML SaaS to combat FinCrime
Several AML/KYC SaaS platforms have evolved to combat increasingly sophisticated fraud. Some focus on user onboarding, while others focus on remote identity verification or biometric solutions. Nonetheless, a number of AML/KYC suppliers have adopted the London-based SwiftDil’s one-stop solution approach. Prior to it, businesses had to integrate with many providers to meet their AML/KYC requirements because most providers only offered feature-limited platforms and APIs with ambiguous pricing. One-stop solutions strive to cover all of a company’s AML/KYC needs with a single API or SaaS offering, usually encompassing AML checks, customer screening, and biometric verification. SwiftDil, which disrupted the AML/KYC compliance market in 2016 with an AI-driven and feature-rich Software as a Service (SaaS), continues to provide one of the most comprehensive and innovative SaaS systems available. In certain countries, it has recently expanded its SaaS service with superior liveness detection technologies and 2+2 verification.
FinTech, legal, telecoms, financial services, recruitment, insurance, healthcare, e-commerce, cryptocurrency, travel, gig economy, and other industries increasingly use it.
Financial Security in the Future
As a large amount of data becomes more readily available, machine learning is beginning to migrate to the cloud. Specialized coding and infrastructure management will no longer be the responsibility of data scientists. AI and machine learning will allow systems to scale to meet their needs, generate new models on the fly, and provide more accurate and rapid results.