Fintech

Artificial Intelligence Becomes Key to Detecting Financial Statement Fraud in the Digital Age – Fintech Schweiz Digital Finance News

Published

9 months ago

22 July, 2024

FinCrypt Staff

Artificial Intelligence Becomes Key to Detecting Financial Statement Fraud in the Digital Age - Fintech Schweiz Digital Finance News

Free Newsletter

Get the latest Swiss Fintech news once a month in your inbox

The proliferation of technology in modern businesses has created new opportunities for financial statement fraud, but has also provided sophisticated tools to detect and prevent such fraud.

In particular, artificial intelligence (AI)-based approaches have the potential to be more efficient and accurate at identifying fraud, especially new patterns that traditional methods may miss, according to a recent paper by Karina Kasztelnik, PhD, and Eva K. Jermakowicz, PhD, CPA, of Tennessee State University in Nashville.

The article, published in June, explores the evolving landscape of financial statement fraud detection, highlighting the role of artificial intelligence in improving the accuracy and efficiency of identifying fraudulent activity compared to traditional methods.

Fraud in financial statements

Financial statement fraud involves the intentional creation of false or misleading information on financial statements. It is typically perpetrated by owners or managers to deceive stakeholders and is intended to present a false picture of a company’s financial health, often to inflate stock prices, meet financial goals, or secure favorable financing terms.

Although financial statement fraud is one of the least frequent types of fraud, its impact can be serious. Several real cases demonstrate this.

Wirecard, a German payment processing company, collapsed in June 2020 after it was revealed that €1.9 billion allegedly held in its accounts had disappeared, leading to its insolvency and the arrest of several executives on fraud and embezzlement charges. The company had inflated its revenues and profits to deceive investors and creditors.

Wells Fargo Employees created millions of unauthorized bank and credit card accounts between 2002 and 2016 to meet aggressive sales targets, without customers’ knowledge or consent. This led to widespread legal and regulatory repercussions, including a $3 billion settlement in 2020, significant fines, and a major overhaul of the bank’s management and practices.

Finally, Enron, once a successful energy company, collapsed in December 2001, after it was revealed that it had committed widespread accounting fraud to hide its financial losses and inflate its earnings. The scandal led to the company’s bankruptcy, the conviction of several top executives, and the implementation of new regulations to improve corporate accountability and financial transparency.

The challenge of detecting fraud in financial statements

Detecting financial statement fraud is a multifaceted challenge due to the complexity and adaptability of fraudulent schemes, the complexity and volume of financial data, inherent human limitations, and the ever-evolving nature of fraudulent activity.

First, financial statement fraud schemes are becoming increasingly sophisticated, making them difficult to detect. Fraudsters often have in-depth knowledge of their company’s operations and internal controls, allowing them to design complex schemes that are well hidden in normal financial reporting processes and difficult to detect.

Second, the volume and complexity of financial data further complicate fraud detection. Modern businesses generate huge amounts of financial data, and financial statements often include complex transactions, multiple subsidiaries, and various forms of accounting treatments, making it difficult to identify irregularities without advanced tools. This overwhelms traditional analysis methods.

Human limitations also play a significant role in the challenge of detecting fraud. Auditors have limited time and resources to conduct detailed examinations of every transaction and financial statement item. As a result, they may miss subtle signs of fraud, especially when dealing with large data sets or when the fraud involves collusion between multiple parties.

Finally, fraud techniques are constantly evolving. As detection methods improve, fraudsters develop new techniques to circumvent these measures, creating an ever-evolving challenge.

Artificial Intelligence-Based Approaches for Financial Statement Fraud Detection

According to the report, modern AI-based approaches are emerging as powerful technologies for more accurate and efficient fraud detection, in the context of ever-evolving fraud patterns and increasing amount and complexity of financial data.

Artificial intelligence encompasses a range of techniques, including machine learning (ML), natural language processing (NLP), robotic process automation (RPA), computer vision, and expert systems. These techniques enable machines to analyze large amounts of data, learn from experience, and make decisions based on changing patterns and rules.

Machine learning (ML), a subset of AI, involves developing algorithms to recognize patterns in data and make predictions or decisions based on those patterns; NLP, another subfield of AI, deals with the interaction between computers and human languages, focusing on unstructured data; and data mining involves using statistical techniques and ML to extract meaningful information from large data sets.

RPA involves the use of software robots to automate tasks performed by humans and improve efficiency, while finally, predictive analytics, a subset of data analytics, involves the use of statistical algorithms and machine learning to examine historical data and make predictions about future events or behaviors.

Advantages of Artificial Intelligence Techniques

According to the report, artificial intelligence and data mining techniques offer significant advantages over traditional methods.

AI approaches use ML algorithms to learn from past examples of fraudulent and non-fraudulent financial data. These algorithms can automatically detect patterns and anomalies in data without relying on predefined rules and are better at detecting new and previously unknown fraud patterns, adapting to changes in the data and fraud landscape over time.

Additionally, AI can analyze large volumes of data faster and more accurately than humans can manually. This allows AI models to detect fraud faster and more efficiently, reducing an entity’s financial losses.

In comparison, traditional rule-based approaches rely on a set of predefined rubrics that are programmed to detect specific patterns or anomalies in financial data. These rules are typically based on expert knowledge and experience and require human intervention to update or modify the rules when new fraud patterns emerge.

AI-vs-traditional-methodologies-for-detecting-fraud-in-financial-reporting-Source-Karina-Kasztelnik-PhD- Artificial Intelligence Versus Traditional Methodologies for Detecting Fraud in Financial Statements, Source: Karina Kasztelnik, PhD, and Eva K. Jermakowicz, PhD, CPA, of Tennessee State University, via CPA Journal, June 2024

Featured image credit: edited by free

Source

Fintech

US Agencies Request Information on Bank-Fintech Dealings

Published

9 months ago

30 July, 2024

FinCrypt Staff

Federal banking regulators have issued a statement reminding banks of the potential risks associated with third-party arrangements to provide bank deposit products and services.

The agencies support responsible innovation and banks that engage in these arrangements in a safe and fair manner and in compliance with applicable law. While these arrangements may offer benefits, supervisory experience has identified a number of safety and soundness, compliance, and consumer concerns with the management of these arrangements. The statement details potential risks and provides examples of effective risk management practices for these arrangements. Additionally, the statement reminds banks of existing legal requirements, guidance, and related resources and provides insights that the agencies have gained through their oversight. The statement does not establish new supervisory expectations.

Separately, the agencies requested additional information on a broad range of arrangements between banks and fintechs, including for deposit, payment, and lending products and services. The agencies are seeking input on the nature and implications of arrangements between banks and fintechs and effective risk management practices.

The agencies are considering whether to take additional steps to ensure that banks effectively manage the risks associated with these different types of arrangements.

SUBSCRIBE TO THE NEWSLETTER

And get exclusive articles on the stock markets

Source

Fintech

What changes in financial regulation have impacted the development of financial technology?

Published

9 months ago

30 July, 2024

FinCrypt Staff

Exploring the complex landscape of global financial regulation, we gather insights from leading fintech leaders, including CEOs and finance experts. From the game-changing impact of PSD2 to the significant role of GDPR in data security, explore the four key regulatory changes that have reshaped fintech development, answering the question: “What changes in financial regulation have impacted fintech development?”

PSD2 revolutionizes access to financial technology
GDPR Improves Fintech Data Privacy
Regulatory Sandboxes Drive Fintech Innovation
GDPR Impacts Fintech Data Security

PSD2 revolutionizes access to financial technology

When it comes to regulatory impact on fintech development, nothing comes close to PSD2. This EU regulation has created a new level playing field for market players of all sizes, from fintech startups to established banks. It has had a ripple effect on other markets around the world, inspiring similar regulatory frameworks and driving global innovation in fintech.

The Payment Services Directive (PSD2), the EU law in force since 2018, has revolutionized the fintech industry by requiring banks to provide third-party payment providers (TPPs) with access to payment services and customer account information via open APIs. This has democratized access to financial data, fostering the development of personalized financial instruments and seamless payment solutions. Advanced security measures such as Strong Customer Authentication (SCA) have increased consumer trust, pushing both fintech companies and traditional banks to innovate and collaborate more effectively, resulting in a dynamic and consumer-friendly financial ecosystem.

The impact of PSD2 has extended beyond the EU, inspiring similar regulations around the world. Countries such as the UK, Australia and Canada have launched their own open banking initiatives, spurred by the benefits seen in the EU. PSD2 has highlighted the benefits of open banking, also prompting US financial institutions and fintech companies to explore similar initiatives voluntarily.

This has led to a global wave of fintech innovation, with financial institutions and fintech companies offering more integrated, personalized and secure services. The EU’s leadership in open banking through PSD2 has set a global standard, promoting regulatory harmonization and fostering an interconnected and innovative global financial ecosystem.

Looking ahead, the EU’s PSD3 proposals and Financial Data Access (FIDA) regulations promise to further advance open banking. PSD3 aims to refine and build on PSD2, with a focus on improving transaction security, fraud prevention, and integration between banks and TPPs. FIDA will expand data sharing beyond payment accounts to include areas such as insurance and investments, paving the way for more comprehensive financial products and services.

These developments are set to further enhance connectivity, efficiency and innovation in financial services, cementing open banking as a key component of the global financial infrastructure.

Sebastian Malczyk

General Manager, Technology and Product Consultant Fintech, Insurtech, Miquido

GDPR Improves Fintech Data Privacy

Privacy and data protection have been taken to another level by the General Data Protection Regulation (GDPR), forcing fintech companies to tighten their data management. In compliance with the GDPR, organizations must ensure that personal data is processed fairly, transparently, and securely.

This has led to increased innovation in fintech towards technologies such as encryption and anonymization for data protection. GDPR was described as a top priority in the data protection strategies of 92% of US-based companies surveyed by PwC.

Arid Islam

Financial Expert, Sterlinx Global

Regulatory Sandboxes Drive Fintech Innovation

Since the UK’s Financial Conduct Authority (FCA) pioneered sandbox regulatory frameworks in 2016 to enable fintech startups to explore new products and services, similar frameworks have been introduced in other countries.

This has reduced the “crippling effect on innovation” caused by a “one size fits all” regulatory approach, which would also require machines to be built to complete regulatory compliance before any testing. Successful applications within sandboxes give regulators the confidence to move forward and address gaps in laws, regulations, or supervisory approaches. This has led to widespread adoption of new technologies and business models and helped channel private sector dynamism, while keeping consumers protected and imposing appropriate regulatory requirements.

George Blandford

Co-founder, UK Linkology

GDPR Impacts Fintech Data Security

A big change in financial regulations that has had a real impact on fintech is the 2018 EU General Data Protection Regulation (GDPR). I have seen how GDPR has pushed us to focus more on user privacy and data security.

GDPR means we have to handle personal data much more carefully. At Leverage, we have had to step up our game to meet these new rules. We have improved our data encryption and started doing regular security audits. It was a little tricky at first, but it has made our systems much more secure.

For example, we’ve added features that give users more control over their data, like simple consent tools and clear privacy notices. These changes have helped us comply with GDPR and made our customers feel more confident in how we handle their information.

I believe that GDPR has made fintech companies, including us at Leverage, more transparent and secure. It has helped build trust with our users, showing them that we take data protection seriously.

Dr. Rhett Stubbendeck

CEO & Co-Founder, Leverage Planning

Source

Fintech

M2P Fintech About to Raise $80M

Published

9 months ago

30 July, 2024

FinCrypt Staff

Application Programming Interface (API) Infrastructure Platform M2P Financial Technology has reached the final round to raise $80 million, at a valuation of $900 million.

Specifically, M2P Fintech, formerly known as Yap, is closing a new funding round involving new and existing investors, according to entrackr.com. The India-based company, which last raised funding two and a half years ago, previously secured $56 million in a round led by Insight Partners, earning a post-money valuation of $650 million.

A source indicated that M2P Fintech is ready to raise $80 million in this new funding round, led by a new investor. Existing backers, including Insight Partners, are also expected to participate. The new funding is expected to go toward enhancing the company’s technology infrastructure and driving growth in domestic and international markets.

What does M2P Fintech do?

M2P Fintech’s API platform enables businesses to provide branded financial services through partnerships with fintech companies while maintaining regulatory compliance. In addition to its operations in India, the company is active in Nepal, UAE, Australia, New Zealand, Philippines, Bahrain, Egypt, and many other countries.

Another source revealed that M2P Fintech’s valuation in this funding round is expected to be between USD 880 million and USD 900 million (post-money). The company has reportedly received a term sheet and the deal is expected to be publicly announced soon. The Tiger Global-backed company has acquired six companies to date, including Goals101, Syntizen, and BSG ITSOFT, to enhance its service offerings.

According to TheKredible, Beenext is the company’s largest shareholder with over 13% ownership, while the co-founders collectively own 34% of the company. Although M2P Fintech has yet to release its FY24 financials, it has reported a significant increase in operating revenue. However, this growth has also been accompanied by a substantial increase in losses.

Source

Fintech

Scottish financial technology firm Aveni secures £11m to expand AI offering

Published

9 months ago

30 July, 2024

FinCrypt Staff

Aveni, Investment Management, AI, NLP, UK

By Gloria Methri

Today

To come
Aveni Assistance
Aveni Detection

Artificial intelligence Financial Technology Aveni has announced one of the largest Series A investments in a Scottish company this year, amounting to £11 million. The investment is led by Puma Private Equity with participation from Par Equity, Lloyds Banking Group and Nationwide.

Aveni combines AI expertise with extensive financial services experience to create large language models (LLMs) and AI products designed specifically for the financial services industry. It is trusted by some of the UK’s leading financial services firms. It has seen significant business growth over the past two years through its conformity and productivity solutions, Aveni Detect and Aveni Assist.

This investment will enable Aveni to build on the success of its existing products, further consolidate its presence in the sector and introduce advanced technologies through FinLLM, a large-scale language model specifically for financial services.

FinLLM is being developed in partnership with new investors Lloyds Banking Group and Nationwide. It is a large, industry-aligned language model that aims to set the standard for transparent, responsible and ethical adoption of generative AI in UK financial services.

Following the investment, the team developing the FinLLM will be based at the Edinburgh Futures Institute, in a state-of-the-art facility.

Joseph Twigg, CEO of Aveniexplained, “The financial services industry doesn’t need AI models that can quote Shakespeare; it needs AI models that deliver transparency, trust, and most importantly, fairness. The way to achieve this is to develop small, highly tuned language models, trained on financial services data, and reviewed by financial services experts for specific financial services use cases. Generative AI is the most significant technological evolution of our generation, and we are in the early stages of adoption. This represents a significant opportunity for Aveni and our partners. The goal with FinLLM is to set a new standard for the controlled, responsible, and ethical adoption of generative AI, outperforming all other generic models in our select financial services use cases.”