Leading UK Personal Injury Leader Scales Intelligence with Toca.ai

This case study shows how Toca.ai transformed a critical, resource-intensive data analysis process for a leading UK personal injury firm, achieving massive efficiency gains and better accuracy in claim defensibility.

The challenge: costly and inconsistent manual data analysis

The client, a large litigation firm specialising in accident claims, needed a detailed analysis of claimant bank transactions to assess their financial status (impecuniosity). The process had become unsustainable and risky because of:

• Massive headcount and cost: collating and categorising claimant bank transaction data was done by hand by a dedicated team of approximately 70 case handlers. This labour-intensive operation consumed significant resources, eating into profitability as the amount claimable per accident declined.
• A processing bottleneck: the manual review created a major bottleneck in processing claims, resulting in slow turnaround times.
• Accuracy and defensibility risk: the process was prone to human error, leading to inconsistent data quality and speed. This inconsistency meant the firm’s impecuniosity assessments were increasingly challenged by defendants, highlighting the critical need for a consistent, accurate and defensible approach.

End-to-end AI automation, in partnership with Jiva.ai

Toca.ai provided the enterprise platform for orchestration and automation, deploying a sophisticated AI solution to categorise and analyse bank transactions, integrated with the firm’s existing systems.

Key solution components

1. Orchestration platform: Toca.ai provided the core infrastructure to integrate all systems, manage the complex workflow, and deploy the AI models.
2. AI and ML pipeline development: the Jiva team applied its expertise to create high-performance machine learning pipelines, combining OCR, natural language processing (NLP) and machine learning (ML) to extract, validate, categorise and analyse the financial data: a level of development that would be an extraordinary effort with traditional coding frameworks.
3. Intelligent data ingestion:
   • OCR and regex preprocessing: to handle non-digital submissions, the system uses optical character recognition (OCR) and regex preprocessing to convert poor-quality scanned bank statements (PDFs and images) into structured, normalised data.
   • Open Banking integration: automated API connections were established with the existing Open Banking data provider.
4. LLM-driven categorisation: a large language model (LLM) embedded in the Toca.ai platform analyses and categorises transaction text based on key data points, identifying trends like salary, recurring payments, transfers and specific government payments.
5. Confidence scoring and auditability: the system applies a confidence score to every result. Only high-confidence outputs are pushed automatically to the firm’s case management system (CMS). Uncertain results go back to case handlers for quick manual review, giving both maximum automation and guaranteed accuracy.

Before and after

Before Spectral, analysing one claimant’s financial history could take three to four hours of focused human effort. Now the same task is completed in seconds, with automatic categorisation and instant integration into the firm’s systems. Case handlers focus on nuanced analysis and exceptions, not repetitive data entry, making their roles more meaningful and strategic.

Impact and results

The implementation delivered rapid and quantifiable improvements, transforming a major cost centre into a reliable, scalable asset.

Metric	Before automation	After automation	Improvement
Manual workload	70 case handlers	Potential reduction of dozens	Significant cost reduction
Process speed	Hours or days of human time	Seconds for querying new data	20x acceleration
Operational costs	High headcount expense	Hundreds of hours saved monthly	Expected ROI of more than 200%
Data lifting accuracy	Prone to human error	76% accuracy	Major risk reduction
Financial status prediction	Inconsistent, challenged	Approximately 85% accuracy	High defensibility

Qualitative outcomes

• Improved compliance and defensibility: the full audit trail and consistent, automated method significantly improved the auditability and defensibility of impecuniosity assessments against challenges from opposing counsel.
• Enhanced scale: the firm can handle more cases without a proportional increase in staff, enabling growth.
• Faster case turnaround: reduced processing time removes the claims bottleneck, resulting in better user satisfaction.

The core automation pipeline, built in collaboration with Jiva, now runs in seconds, giving the firm the consistent, accurate and scalable data analysis foundation it needs for sustained profitability.

Ready to transform your firm’s operations?

If you’re dealing with high-volume, repetitive processes that need intelligence but not necessarily years of legal training, Toca AI can help. Learn more at toca.io and jiva.ai.

This case study features a leading UK personal injury law firm that has chosen to remain anonymous. Technical implementation delivered in partnership with Jiva, Toca’s AI development partner.