Featurespace has launched TallierLTM, the world’s first Large Transaction Model (LTM), designed with AI to power the “next generation of AI applications for the financial protection of consumers”.
TallierLTM provides an improvement when it comes to differentiating between genuine consumers and bad actors. 70% of financial institutions in North America consider financial criminal attacks to be getting worse as they become more sophisticated with exponentially increasing losses.
“What OpenAI’s LLMs have done for language, TallierLTM will do for payments,” said David Excell, Founder of Featurespace.
“There is widespread concern about how deep-fakes and generative AI have been used to deceive consumers and our financial systems. We plan to reverse this trend by utilising the power of generative AI algorithms to create solutions that protect consumers and make the world a safer place to transact.”
TallierLTM has been pre-trained across jurisdictions and market segments using a self-supervised approach, making it accurate and representative of real-world consumer transactions.
By analysing up to billions of transactions, TallierLTM identifies hidden transactional patterns using current industry methods, enabling it to generate likely future consumer transactions. Insights are based on time sequencing, such as unusual spending patterns over a short period of time, and patterns of behaviour between a consumer and a merchant.
Financial institutions will be able to interact with TallierLTM via its embedding API, which is a data science accelerator that enables a consumer’s transaction history to be converted to a machine-readable feature vector.
It creates a unique ‘behavioural bar code,’ providing a comprehensive representation of a consumer’s transactional behaviour without revealing any personally identifiable information.
Dr. David Sutton, Featurespace’s Chief Innovation Officer, added: “We know that smarter technology helps financial institutions better understand their consumers. We have taken this to the next level by pairing cutting-edge generative AI algorithms with huge volumes of data, enabling a machine to efficiently comprehend the relationships between different customer transactions.
“By adding TallierLTM’s feature vectors to the inputs of an industry-standard fraud model, we’ve seen improvements of up to 71% in fraud value detection. This will accelerate data science teams’ ability to level up their model performance and realise the value of machine learning investments more quickly.”