AI Driven Customer Engagement – Morphiq AI

Transactions is all you need

Transformers in Financial Recommenders. Transactions is all you need

The vast expanse of the financial sector encompasses services like personal banking, credit cards, loans, insurance, investment products, wealth management and other. Intriguingly, individual interactions with these services can diverge widely, even when demographic profiles are strikingly similar.

This prompts an essential inquiry: Can financial recommendation systems, powered by Large Language Models (LLMs), be built purely on transactional data, sidelining the conventional reliance on demographics?

Why transactional data over demographics?

  1. Universal Behavioral Patterns: Two seemingly different individuals, regardless of age, gender, location or socioeconomic status might display similar financial patterns. For instance, both might prioritize sustainable investing, spend conservatively or have an affinity for shopping.
  2. Real-time Adaptability: As individuals evolve so do their financial habits morph over time. A person might transition from being a spendthrift in their 20s to a conservative spender in their 30s. Transactional data with its real-time insights, caters to these dynamic shifts, enabling adaptive and timely recommendations.
  3. A focus on privacy: With increasing concerns about personal data security and privacy, a system that focuses on transactional patterns rather than intimate demographic details can resonate better with users’ comfort levels.

The edge of transformer models like LLMs

LLMs operate on a principle that is inherently sequential, much like the progression of scenes in a movie. Just as an LLM predicts the next most probable token in a sentence based on prior context, it can also be visualized as predicting the next frame in a video, building on all the previous frames to create a coherent narrative.

In a similar vein, when used for financial recommendations the LLM analyzes past transactions to forecast the next ‘logical’ recommendation. This is akin to predicting the subsequent scene in a movie based on all the preceding scenes.

It’s like the past transactions set the context and the “storyline” and the LLM’s recommendation can be equated to the next scene, ensuring continuity and relevance.

Benefits of utilizing LLMs in Financial Recommendations

Complex pattern recognition – LLMs can analyze vast amounts of transactional data to identify intricate patterns that might be overlooked by traditional algorithms.

Contextual understanding – An LLM can use transaction histories to anticipate financial needs. For instance, frequent transactions related to baby products could suggest a user might be interested in starting a savings plan or purchasing life insurance.

Customization – Each individual’s financial journey is unique. LLMs, with their vast training and adaptability, can offer hyper-personalized financial advice based on transactional behaviors.

In summary:

LLMs transcending their conventional roles of text generation or query response, hold immense potential in reshaping the financial recommendation landscape. This approach not only respects the evolving nature of individual financial behaviors but also paves the way for a more privacy-centric model.

#MorphiqAI #ShieldAI #AlphaML #spendingDNA #Transformers #recommenders

#MorphiqAI is a SaaS platform that employs LLMs and other models to provide hyper-personalized financial advice based on ever changing transactional behaviors

#Shield.AI – see part two of the Article, Security is all you need

#AlphaML – see part one of the Article, Quality is all you need