Structured transaction data offers balanced approach to AI in banking

Like most service providers, banks around the world have seen significant benefits from their move to digital, logging operational expense reduction and customer satisfaction. Financial institutions must, however, operate within a more stringent regulatory framework around accuracy, reliability, and compliance than other business sectors.

As banks now increasingly see the benefits of integrating generative AI into their platforms, financial organizations remain justifiably cautious, especially when dealing with applications that directly impact customers. Concerns arise over data privacy, potential “hallucinations,” or inaccuracies inherent in gen AI models.

That said, FIs understand the substantial potential residing within transaction data. Transforming raw transaction descriptions into structured data for clearly identifying merchants, payees, beneficiaries, and counterparties is increasingly an imperative in financial services. Structured transaction data significantly enhances compliance checks, automates reporting, accelerates fraud detection, and enriches customer insights for tailored services, for instance.

Given these considerations, how can the AI lead at financial institutions strike the right balance between the potential benefits of gen AI and the air of caution? Let’s explore.

Unlocking value through Named Entity Recognition (NER)

Named Entity Recognition (NER) is a powerful Natural Language Processing (NLP) technique that automatically identifies and categorizes entities within text. In banking, NER specifically extracts valuable information such as merchants, payees, account numbers, and transaction references

directly from raw descriptions. The result is structured, actionable data that banks can immediately leverage to:

• Enhance customer experience: Delivering personalized insights based on a customer’s preferred financial behavior.
• Automate compliance: Quickly matching entities against sanction lists or compliance
• Detect fraud: Recognizing irregular patterns by identifying unusual counterparties or transaction frequencies.
• Improve operational efficiency: Streamlining transaction reconciliation and audit

The best of both worlds: Offline innovation and real-time precision

Any solution worth its salt should leverage a unique, dual-strategy approach to AI deployment, combining the strengths of powerful, advanced generative AI models in offline environments with faster, more controllable AI models in real-time production.

Offline: Large state-of-the-art gen AI models run in a secure cloud to handle computationally intensive tasks, enriching the training dataset while remaining fully compliant. For example, they auto-label transactions and generate realistic synthetic data that fills coverage gaps.

Real-time: Offline knowledge is distilled into lightweight transformer models that retain the core language understanding of larger models and deliver high speed inference while keeping error handling and model hallucination more manageable.

By clearly separating offline computational power from online efficiency and accuracy, banks can innovate confidently, advancing technologically while complying with stringent regulations.

Let’s address each of these separately.

Offline implementation: Advanced AI labeling techniques

These impressive performances and results depend heavily on high-quality training data.

Traditionally, generating such data through manual labeling is resource intensive. Advanced labeling processes are available that leverage powerful new large language models (LLMs). Securely hosted within secure cloud environments, they provide a highly accurate and efficient labeling mechanism with the following elements:

1. Labeling agent: A robust LLM-based agent that leverages extensive internet searches and context-awareness through a ‘few-shot’ learning approach to automatically propose precise entity labels from raw transaction descriptions.
2. Validation agent: A separate, independent AI model that evaluates the labels generated by the labelling agent, assigning confidence scores based on consistency and contextual accuracy.

3. Synthetic data agent: For transactions lacking sufficient real-world examples, another specialized LLM generates realistic synthetic data, filling gaps in the training dataset and ensuring comprehensive scenario coverage.
4. Human in the Loop: High-confidence labels are automatically approved, optimizing efficiency. Labels with lower confidence scores or edge cases undergo expert human review to validate and correct errors, ensuring data integrity and training quality. This meticulous labelling framework significantly accelerates data preparation, providing models with highly reliable and diverse training datasets while leveraging advanced generative AI securely and effectively.

Real-time implementation: AI models for immediate accuracy

Open-source AI models, such as DistilBERT, DistilRoBERTa, MiniLM, and TinyBERT, provide an optimal balance between speed and linguistic depth. Once fine-tuned on high quality, domain specific data, they offer remarkable inference speed combined with deep linguistic accuracy. Such models rapidly analyze transaction descriptions, extracting exact substrings that identify counterparties. Although any language model can occasionally “hallucinate,” these lightweight transformer models make those errors far easier to detect and control—approaching zero hallucinations—and so deliver consistent, accurate outputs.

Since these streamlined models run entirely inside the bank’s secure environment, they keep customer data in-house, produce auditable, deterministic outputs, and rely on far fewer external dependencies. The result is low-latency processing and an architecture that better aligns with data- residency, model-governance, and security regulations.

A strategic AI opportunity for banking

Banks need not choose between innovation and compliance. The hybrid approach demonstrates that carefully structured AI solutions can effectively transform banking operations safely and compliantly.

By strategically aligning advanced offline processes with precise real-time models, banks can confidently leverage transaction data for immediate insights and stronger compliance, while enhancing customer experiences.

Dedi Kovach is Data Science & AI Lead at Personetics.