This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play, tree-of-thought, and critique-based refinement techniques show early promising results that each idea's success patterns are unique and they should be evaluated based on the context of the founder's background.

Clouds are gathering over swaths of fintech companies, as falling economic growth, rising interest rates and a cost of living crisis put their business models under strain, forcing job cuts and valuation-crushing funding rounds. ComplyAdvantage founder Charlie Delingpole knows his company is not immune to those forces, as fintechs are among the biggest buyers of his financial crime prevention products. In fact, some clients, including crypto lender Celsius Network, have already gone bust. But the business -- which uses natural language processing and artificial intelligence (AI) to run compliance checks on transactions -- is proving more resilient than most, as Russia-related sanctions and a global clampdown on financial crime underpin healthy demand. "We're the last thing they turn off before their server," says Delingpole, a one-time JPMorgan Chase technology banker, of the enduring demand for his company's services from financial groups -- even when times are tight.

Artificial intelligence has demonstrated immense promise when applying machine learning to support the overall processing of large datasets, particularly in the banking and financial services industry. Sixty percent of financial services companies have implemented at least one form of AI, ranging from virtual assistants communicating with customers and the automation of workflows to managing fraud and network security. This largely stems from lack of understanding of how the system works and a continual concern around opacity, unfair discrimination, ethics and dangers to privacy and autonomy. These biases can create increased consumer friction, poor customer service, fewer sales and revenue, unfair or illegal behaviors, and potential discrimination. Achieving trustworthy AI requires close examination and the ability to identify what factors contribute to each bias to make a more informed decision about what actions should be taken after identification.

Every large financial services company is pursuing AI initiatives in 2022. Some are in the earliest stages. Others have been investing in the technology for years. Regardless, all recognize that natural-language processing (NLP) is a foundational capability for their long-term AI business goals. Unfortunately NLP is still an emerging technology, and the best options for deploying it are not immediately obvious.

Every large financial services company is pursuing AI initiatives in 2022. Some are in the earliest stages. Others have been investing in the technology for years. Regardless, all recognize that natural-language processing (NLP) is a foundational capability for their long-term AI business goals. Unfortunately NLP is still an emerging technology, and the best options for deploying it are not immediately obvious.

Some 80% of the world's top financial firms are spending billions on artificial intelligence to improve their services and compete with each other. New research from NVIDIA uncovers what those firms are doing and how they're deploying these resources. Competition for consumers and their financial data continues to intensify across incumbent banks, fintech, big tech, and big-box retail. This is compounded by highly innovative digital experiences being deployed across industries, which continue to shift consumer expectations. Kevin Levitt, head of NVIDIA Financial Services says that financial services companies must enhance the level of personalisation, data security, customer service, pricing, and more in the creation and delivery of financial products or expect to lose market share to those who do.

Technology has dramatically changed how the financial services industry operates. This has been consistent over many decades; however, recently the pace of change has become exceptionally fast. The fintech market has deployed these technologies to disrupt the broader industry by enhancing the customer experience and changing the traditional customer acquisition model. The next evolution of fintech will focus on the back end and middleware software that powers the financial services industries. What few market participants realize is the shiny front-end customer experience of these new fintechs and neobanks are still often powered by traditional banking systems.

People have become very familiar with the term artificial intelligence (AI), but many of its users have only a rudimentary understanding of how it actually works. As a result, to date financial services and many other industries have yet to leverage its full capabilities. For financial services firms, adoption of explainable AI could drive adoption of AI-related technologies from the current rate of 30% to as high as 50% in the next 18 months, according to Gartner analyst and vice president Moutusi Sau, adding that lack of explainability is inhibiting financial services providers from adopting/rolling out pilots and projects in lending and from offering more products to the "underbanked" -- those who don't seek banking products or services, many because they don't think they will qualify. Moving to "explainable AI" will remove much of the mystery around AI, and, as a result will drive adoption of more AI-driven services experts agree. The Global Explainable AI (XAI) market size is estimated to grow from $3.50 billion in 2020 to $21.03 billion by 2030, according to ResearchandMarkets.

According to The Payments Association, the AI market will hit US $360 billion by the year 2028. Their recent survey outlines how AI and machine learning are currently used in payments, finance and banking sectors and how they can be used in the future. The research conveyed that the interest in AI and its acceptance were slow but sustained prior to COVID-19, but have seen a surge in interest since the pandemic as financial institutions look for ways to increase their efficiencies. This has led to a surge in regulatory interest in AI to mitigate privacy, illegal discrimination and security-related risks. Backed by the international payments firm, Fable Fintech, this report shows that the majority of financial services companies with more than 5,000 employees are using some form of AI.

CHICAGO, April 15, 2021 (GLOBE NEWSWIRE) -- ModelOp, the pioneer of ModelOps software for major enterprises, today announces release of the first annual State of ModelOps report. Conducted by independent research firm Corinium Intelligence, the report summarizes the first ever research into the state of model operationalization and details the challenges faced by AI-focused executives from top global financial services companies as they scale their AI initiatives. Findings show that while AI is already widely deployed in many large enterprises and investments are growing, nearly 80 percent of surveyed executives reported difficulty managing risk as a barrier to AI adoption, and cite ModelOps as a key enterprise discipline that is receiving significantly increased attention and investment. The report is based on interviews with 100 executives from top global financial services companies in early 2021, providing a unique snapshot of the practices and future plans of large enterprises to govern and scale mission-critical AI initiatives. The findings were combined with commentary and insight from seven industry experts from organizations including Wells Fargo Asset Management, NY Life Insurance, BNY Mellon, FICO and others.