TIMeSynC: Temporal Intent Modelling with Synchronized Context Encodings for Financial Service Applications

TL;DR

This paper introduces TIMeSynC, a transformer-based model that effectively aligns and learns from multi-channel, multi-resolution sequential data to improve intent prediction in financial services.

Contribution

The paper presents a novel encoder-decoder transformer architecture that addresses sequence alignment, temporal dynamics, and integration of static and dynamic data for intent prediction.

Findings

Significant improvement over existing tabular methods.

Effective handling of multi-resolution, multi-domain sequences.

Enhanced intent prediction accuracy in financial applications.

Abstract

Users engage with financial services companies through multiple channels, often interacting with mobile applications, web platforms, call centers, and physical locations to service their accounts. The resulting interactions are recorded at heterogeneous temporal resolutions across these domains. This multi-channel data can be combined and encoded to create a comprehensive representation of the customer's journey for accurate intent prediction. This demands sequential learning solutions. NMT transformers achieve state-of-the-art sequential representation learning by encoding context and decoding for the next best action to represent long-range dependencies. However, three major challenges exist while combining multi-domain sequences within an encoder-decoder transformers architecture for intent prediction applications: a) aligning sequences with different sampling rates b) learning temporal dynamics across multi-variate, multi-domain sequences c) combining dynamic and static sequences. We propose an encoder-decoder transformer model to address these challenges for contextual and sequential intent prediction in financial servicing applications. Our experiments show significant improvement over the existing tabular method.