How Large AI Models Unlock New Opportunities in Digital Finance with a 6D Methodology

1. Bank Business Case: Staff's "Digital Dilemma"

Bank loan officer Xiao Hua, responsible for credit products across multiple financial segments, must repeatedly request data from the data team to compile loan‑allocation statistics, perform root‑cause analysis, and forecast future allocations. The reliance on data‑team scheduling creates delays and hampers rapid response to market changes, a situation the authors label the staff’s “digital dilemma.”

2. Large‑Model‑Powered Data Insight: NL2SQL and Root‑Cause Analysis

When Xiao Hua submitted another data request, the data team introduced the newly developed large‑model application ChatBI, which converts natural‑language queries into Structured Query Language (NL2SQL). The system leverages the model for three assistance layers:

Data Governance : Metadata, data dictionaries, and other governance artifacts are placed in a Retrieval‑Augmented Generation (RAG) knowledge base, allowing the model to tag and locate relevant tables and fields automatically.

Data Security : By analysing lifecycle records of data storage, movement, and usage, the model builds a data‑security map and checks the requester’s permissions before releasing results.

Data Analytics : Financial terminology in the natural‑language request is matched to database schema elements, generating a set of high‑quality SQL candidates, which the model then validates, corrects, and selects for execution.

Using ChatBI, Xiao Hua asked, “Please query the Q1 loan allocation for the pension‑finance segment and analyse why it changed compared with Q4 last year.” The system instantly returned the statistics and a plausible cause analysis, eliminating the need for data‑team scheduling.

3. Large‑Model‑Enabled Discriminative AI: Credit‑Risk and Allocation Forecasting

To predict future loan volumes and credit risk, Xiao Hua asked the model to “forecast Q2 pension‑finance loan allocation and credit risk.” The model automatically assembled a discriminative AI pipeline:

Data Analytics generated complex SQL to create a wide, structured table suitable for machine‑learning.

Prompt templates describing feature engineering, modeling algorithms (e.g., logistic regression, decision trees), and evaluation criteria were fed to the model.

The model decomposed the task, wrote data‑preparation, model‑training, and evaluation code, and executed it via a local code interpreter, delivering the prediction results.

This self‑service approach lets Xiao Hua obtain timely forecasts without involving data engineers.

4. Large‑Model‑Assisted Decision Intelligence: Digital Twin & Simulation

Asset‑liability manager Xiao Xia needed optimal interest‑rate pricing for the bank’s asset side. Together with the data team, they built a digital‑twin simulation environment using historical data and machine‑learning models to estimate loan volumes and credit‑risk outcomes under varying rate scenarios.

Leveraging the digital twin, Xiao Xia could explore macro‑economic shifts and rate‑mix strategies, evaluating their impact on profitability and non‑performing‑loan ratios. The model also introduced decision‑intelligence agents that iteratively adjusted pricing choices within the simulation to maximise overall returns while keeping risk constraints.

5. The "6D" Methodology for Merging Large Models and Big Data

The case study culminates in a six‑component framework (illustrated in Figure 1) that structures the integration of large models with big‑data assets in banking:

Data Governance : Automated classification, tagging, and integration of data assets.

Data Security : Lifecycle‑wide risk detection and permission enforcement.

Data Analytics : Natural‑language‑driven query generation, error correction, and insight extraction.

Discriminative AI : Model‑building pipelines for credit‑risk and allocation forecasting.

Digital Twin & Simulation : Synthetic environments for scenario testing and strategic planning.

Decision Intelligence & Agents : Autonomous agents that optimise complex workflow decisions.

Each component reduces manual effort, shortens data‑to‑insight cycles, and lowers operational costs.

6. Conclusion

Advances in large‑model technology provide strong technical support for digital finance. By freeing staff like Xiao Hua from repetitive data‑request processes and enabling Xiao Xia to optimise rate‑pricing through intelligent agents, the 6D framework demonstrates a practical pathway for banks to accelerate digital‑finance innovation, improve service quality, and achieve more data‑driven decision making.