Artificial intelligence (AI) is all the rage now. It's impacting numerous industries globally and changing the way we do things. One of the critical industries AI is making strides in is the financial technology "fintech" industry. AI now plays a significant role in facilitating financial services, replacing what required manual work a few years ago. For example, banks now apply AI to assess credit risks with high accuracy.
The Fintech industry's rapid growth and use of new technologies to meet the rise in demand for online services has brought with it increased levels of cyber crime. Criminals have taken advantage of the benefits digital banks offer to access money, launder illicit money and fund terrorism worldwide. The growth in technology for blockchain and digital payments provides new opportunities for criminals to launder funds at faster speeds and larger scales than they might have been able to previously. According to UK Finance, criminals stole a total of £753.9 million through fraud in the first half of 2021, an increase of 30% compared to H1 2020. With the huge amounts of data that Fintechs process, it's no mean feat to detect potential money laundering activities using manual processes.
Multinational technology corporation IBM calculated that 72% of business leaders cited fraud as a growing concern in the last year, that $44 billion will be lost worldwide due to fraud by 2024, and that a quarter of e-commerce sales transactions that were declined by artificial intelligence (AI) were false positives. AI has become the leading tool for fighting fraud, but it can still be improved upon. In the past, rule-based engines and simple predictive models were used to computationally identify the majority of fraud attempts. But these methods have not kept up with the increasingly sophisticated nature of fraud attacks today. With a proliferation of digital technologies at criminals' disposal, fraud has grown in both scale and severity over the last few decades. Large criminal organizations and even state-sponsored groups use AI-like machine learning (ML) algorithms to defraud digital businesses for millions of dollars each year.
Parijat Banerjee is Global Business Head for the Banking, Financial Services, and Insurance (BFSI) sector at LatentView Analytics. The pandemic pushed us years forward, and the notion that every modern company is a technology company has only been reinforced by rapidly transforming business practices. We are at an apex of digital transformation, and precision analytics is driving the most successful innovation initiatives across many industries. The unequivocal rise of the connected ecosystem has forced consumers to engage across digital channels, and banking and financial services evolved almost overnight. The race to digital has always seemingly been easy, but the adoption and implementation remain an uphill climb, particularly in the financial services industry, where legacy solutions and antiquated IT infrastructure have a stranglehold on business processes. Over the last 18 months, exponential improvements in digital technology have underpinned the evolution of banking for both internal processes and customer-facing experiences.
This week's top reads in banking, fintech, payments, cybersecurity, AI, IoT, risk management and much more In this weeks selection; Top Reads Analysts pin Google retail bank U-turn on fears of higher regulatory scrutiny, low profitability JPMorgan Chase joins UN's Net-Zero Banking Alliance Why Chatbots Fail in Banking We may visit you at home, British financial watchdog warns bank staff SocGen to Cut 3,700 Jobs as Part of Domestic Retail Merger Crypto Could be in Trouble after China Declares all Crypto Transactions Illegal Two Key Digital Payments Trends in the Post-COVID World Capgemini's World Payments Report 2021 Are NFTs a Money Laundering Gold Mine? From tech tool to business asset: How banks are using B2B APIs to fuel growth Will massive outage set back Facebook's payments plans? Analysts pin Google retail bank U-turn on fears of higher regulatory scrutiny, low profitability JPMorgan Chase joins UN's Net-Zero Banking Alliance Why Chatbots Fail in Banking We may visit you at home, British financial watchdog warns bank staff SocGen to Cut 3,700 Jobs as Part of Domestic Retail Merger Crypto Could be in Trouble after China Declares all Crypto Transactions Illegal Two Key Digital Payments Trends in the Post-COVID World Capgemini's World Payments Report 2021 Are NFTs a Money Laundering Gold Mine? From tech tool to business asset: How banks are using B2B APIs to fuel growth Will massive outage set back Facebook's payments plans? JPMorgan Chase joins UN's Net-Zero Banking Alliance Are NFTs a Money Laundering Gold Mine?
It is important that credit card companies are able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase. The fraud usually happens when someone obtains your credit or debit card numbers through unprotected websites or through an identity theft scheme in order to get money or property fraudulently. Because of the frequency with which it occurs and the potential harm it may bring to both individuals and financial institutions, it is critical to take preventative steps as well as recognize when a transaction is fraudulent. Data-set can be downloaded from the above link. As the data-set is highly imbalanced, there is a need for us to balance it, in order to get classes to close proximity.
Founder & CEO of MovoCash, Inc., where he's combining the best of banking & blockchain through MOVO, a highly secure payment card platform. Artificial intelligence (AI) has transformed financial services in recent years and is poised to completely revolutionize the world of payments in the near future. With the ability to quickly analyze massive quantities of data to derive important insights and information, AI is used by businesses to create efficiencies and recognize patterns that can improve decision making. The average consumer may not even realize the many ways AI is already being used behind the scenes by the businesses interacted with every day, but as many lives continue to become more connected and more reliant on digital technologies and processes, its use is likely to only increase. This can bring many benefits, such as helping the financial industry fight fraud, deliver better customer experiences and create new efficiencies and conveniences when it comes to payments.
While many fintech platforms focus on risk assessment, Brighterion has been solely dedicated to AI-powered decisioning for over 20 years. With a sharp focus on financial irregularities, Brighterion's AI decision-making algorithms provide real-time detection in financial fraud, credit risk, healthcare fraud, waste and abuse, and money laundering (AML). The role of artificial intelligence is taking top billing in the search for software that detects fraud and credit risk. Legacy solutions like rules-based decisioning are hard pressed to stay ahead of bad actors as fraud evolves and becomes more sophisticated. Machine learning rises to the top for its ability to learn from complex and widely varied data.
AI is revolutionizing how financial institutions and technology companies are using their data to automate repetitive tasks and gain valuable insights. Some popular examples of AI applications in Fintech are fraud detection, risk assessment or virtual financial assistants. With regard to the Data Science Salon for Finance & Technology from December 8–10, we had the chance to talk to leading data scientists in the Fintech industry and ask them about their favorite AI use cases, the power of AI to fight COVID-19 challenges, AI trends for 2021 as well as the challenges of successfully implementing AI projects. Spoiler alert: includes recommendations for data scientists working in the finance and technology fields. "Uncovering money laundering is an indispensable challenge for the financial services organizations and the risks associated with it are regulatory as well as reputational. Money laundering is the process by which someone injects illegal money into a legitimate system by moving large amounts of money to untraceable or counterfeit accounts. Governing such risks will now require more sophisticated algorithms as criminals can easily exploit the loopholes in rule-based guards and evolve their laundering techniques. Thus, keeping in pace with the evolving regulatory environment along with criminal typologies, the compliance teams will have to adopt new technologies to improve their detection and to optimize the analyst workloads. This is an interesting use-case where AI can enhance existing compliance processes by developing various graph-based algorithms where the focus shouldn't only be on dense subgraphs but also on the fact that laundering of money involves flow of money through chains of bank accounts; making high accuracy detection of money laundering highly challenging."