Better Bill GPT Framework

Updated 17 November 2025

Better Bill GPT is a framework that automates and optimizes legal invoice review using LLMs, achieving superior accuracy, speed, and cost-efficiency compared to human reviewers.
It integrates rigorous financial literacy evaluations and robo-advice paradigms, demonstrating high performance and emergent reasoning in financial contexts.
The framework employs a blockchain-based DataBOM for secure, traceable, and reproducible data provenance, enabling robust hybrid human-AI workflows in legal spend management.

Better Bill GPT is a framework for automating, optimizing, and securing the review of legal invoices and related financial advice and AI-training data pipelines using LLMs and blockchain-based provenance mechanisms. Its empirical foundation spans quantitative evaluations of LLM performance in invoice review, rigorous financial literacy benchmarking, user advice integration paradigms, and a machine-readable Data Bill of Materials (DataBOM) architecture for responsible data handling.

1. Legal Invoice Review Automation and Benchmarking

Better Bill GPT centers on automating the traditionally manual and inconsistent process of legal invoice review. The system benchmarks six state-of-the-art LLMs (OpenAI o1, GPT-4o, Claude 3.5 Sonnet, Claude 3.7 Sonnet, Gemini 2.0 Flash Thinking Experimental 01–21, DeepSeek R1) against three human reviewer cohorts: Early-Career Lawyers (<5 years PQE), Experienced Lawyers (≥5 years PQE), and Experienced Non-Lawyers (Legal Ops/Finance, ≥3 years experience).

A dataset of 100 invoices (50 anonymized, 50 synthetic) comprising 492 unique line items was constructed to span key billing categories (timekeepers, expense caps, block-billing, travel, administrative fees) and varied formats. Annotation involved nine expert reviewers for ground-truth generation, with consensus "approve/dispute" labels at both invoice and item levels.

Comparison used standard classification metrics:

Accuracy: $(TP + TN) / (TP + TN + FP + FN)$
Precision: $TP / (TP + FP)$
Recall: $TP / (TP + FN)$
$F_1$ : $2 \cdot (\text{Precision}) \cdot (\text{Recall}) / (\text{Precision} + \text{Recall})$

LLMs systematically surpassed human accuracy, speed, and cost-efficiency:

Reviewer	Invoice F-Score	Item F-Score	Review Time (s)	Cost/invoice (USD)
Gemini 2.0	0.920	0.8058	8.68	$0.00
GPT-4o	0.920	0.6307	3.61	~$0.01
Claude models/o1	0.820–0.840	0.5218–0.6426	6.34–17.87	$0.01–$0.13
DeepSeek R1	0.820	0.5938	21.48	~$0.01
Experienced Lawyer	0.7206	0.4292	194.75	$4.27
Early-Career	0.7024	0.3982	257.50	$4.47
Non-Lawyer	0.6754	0.3677	316.93	$4.17

Strong internal consistencies (Cronbach’s $\alpha = 0.868$ for overall invoice and $0.832$ for items; Fleiss’ $\kappa = 0.331$ for dispute reasons) substantiate ground truth reliability.

2. Financial Literacy Evaluation and Robo-Advice

Expanding beyond invoice review, Better Bill GPT leverages insights from financial literacy evaluations on LLM architectures (Niszczota et al., 2023). Benchmarking employs a 21-item test derived from the "Big Five" (Mitchell & Lusardi, 2022) and Financial Literacy Baseline Survey (Heinberg et al., 2014), encompassing time value, inflation, compound interest, risk diversification, mortgage trade-offs, and asset taxation.

GPT-3.5 series models scored 65–66% versus the chance baseline of ~33%. However, GPT-4 ChatGPT reached 99% accuracy, demonstrating emergent financial reasoning. Prompt sensitivity analysis revealed minor drops under advisory role prompts. Robustness checks (question rephrasing, answer-order permutation) confirmed model consistency (accuracy $\geq$ 96%).

Advice-utilization was measured via a Judge-Advisor paradigm on a savings dilemma. Weight of Advice (WOA) was computed as

$WOA = \frac{\text{FinalAnswer} - \text{InitialAnswer}}{\text{AdvisorAnswer} - \text{InitialAnswer}}$

Mean WOA (winsorized) of 0.650 exceeded the meta-analytic reference of ~0.39, with lower self-reported financial knowledge correlating to higher WOA.

Design limitations identified in (Niszczota et al., 2023) include lack of multi-model comparisons, prompt sensitivity, absence of personalization, and uncertainty reporting.

3. Blockchain-Enabled DataBOM for Training Data Traceability

Better Bill GPT incorporates a blockchain-driven Data Bill of Materials (DataBOM) paradigm (Liu et al., 16 Aug 2024) for dataset provenance in LLM pipelines. DataBOM is defined as a machine-readable bill of materials, representing every artifact or derivative as a node in a directed acyclic graph $G=(V,E)$ , tracking $id_v$ , $parentIDs_v$ , $version_v$ , $checksum_v$ , $merkleRoot_v$ , $timestamp_v$ , $license_v$ , $operator_v$ , $signature_v$ , and $uri_v$ for each dataset ( $v \in V$ ). This enables:

Traceability: clear ancestry and transformation lineage for every corpus.
Verifiability: cryptographic integrity checks using checksums and Merkle root proofs.
Reproducibility: auditors and retrainers can reconstruct training data via provenance queries.
Accountability: stakeholders mapped to identities via blockchain addresses and off-chain identities (DIDs).

Core system architecture encompasses:

Service Layer: orchestration, DataBOM generation, access control, identity management.
On-Chain Layer: smart contracts for identity registration, registry creation, entry management, and optional Merkle root storage.
Off-Chain Data Layer: actual corpora stored in decentralized storage or institutional repositories.

Provenance queries and integrity verification leverage adjacency lists/matrices and Merkle proof algorithms. Performance benchmarks show up to 27 tps retrieval and ~1 tps creation on Ganache/Ethereum-like networks, scalable via permissioned chains, sharding, batching, and off-chain indexing.

4. Hybrid Human-AI Workflows and Organizational Recommendations

Better Bill GPT’s results suggest that traditional manual line-by-line auditing should shift toward AI-driven exception handling. LLMs can perform primary compliance checks, with human experts redeployed for high-value negotiations and oversight, especially on ambiguous or flagged items. Hybrid workflows allow humans to intervene on edge cases requiring contextual analysis (engagement letters, timesheets).

Integration recommendations for legal operations:

API-level insertion into ELM (Enterprise Legal Management) platforms.
Continued human-in-the-loop governance for high-risk or nuanced disputes.
Periodic performance audits to detect and mitigate AI drift, including retraining prompts or model fine-tuning on evolving billing policies.
Longitudinal studies are recommended to determine whether improved AI consistency reduces aggregate dispute rates and modifies outside counsel billing behavior.

5. Safety, Personalization, and Governance Strategies

The design blueprint for Better Bill GPT advises multi-layered safety and governance measures:

Prompt engineering libraries and few-shot anchoring to stabilize advisory behaviors.
Professional domain fine-tuning on specialized legal and financial corpora, plus adversarial training against common hallucination modes.
Augmentation of advice with probabilistic confidence metrics and caveats; automatic human escalation when confidence falls below thresholds.
Real-time user profiling to personalize outputs based on jurisdiction, risk tolerance, and goals.
Regular model benchmarking against alternative LLMs and human professionals for comparative reference.
Micro-surveys and longitudinal metrics to measure user trust, satisfaction, advice adoption, and outcome improvement.
Governance provisions for red-teaming to block illicit use, real-time hallucination rate monitoring, and version rollback mechanisms if models drift from requirements.

Better Bill GPT is positioned for phased rollout: baseline evaluation and prompt creation, corpus fine-tuning, pilot advice deployments with trust metrics, expanded domain coverage, and full governance lifecycle (live monitoring, periodic audits, cross-model benchmarking).

6. Performance, Scalability, and Implications for AI-Powered Legal Spend Management

Empirical evidence from invoice review tasks confirms LLMs decisively outperform humans on all measured fronts (Whitehouse et al., 2 Apr 2025): invoice-level accuracy of 92% (vs. humans' 72%), line-item F-score of 81% (vs. 43%), review speeds 50–80× faster, and per-invoice cost reductions exceeding 99%. Blockhain-enabled DataBOM architectures achieve sub-second provenance queries with scalable transaction throughput.

The practical implications are substantial for law firms and corporate legal departments. These include enhanced compliance enforcement, drastically reduced review cycle times, and significant cost savings. As LLM-driven review becomes standard, firms can anticipate heightened billing scrutiny and greater uniformity in guideline enforcement.

A plausible implication is that adoption of Better Bill GPT will catalyze operational shifts in legal spend management, necessitating strategic approaches to integrating automation with human judgment, and new measurement frameworks for monitoring system drift, outcome quality, and user trust over time.