Girish Keshav Palshikar

India’s insurance dispute resolution system is burdened by long delays, escalating costs, and growing complaint volumes. With the insurance market projected to reach US$ 222 billion by 2026 and recent data showing that leading public insurers registered over 81,000 complaints in FY2023, traditional processes are no longer viable. This paper proposes a “Dispute Resolution as a Service” model for the insurance claim domain, enabled by AI technologies with service design principles. By rethinking dispute resolution as a service, the solution addresses not only the technical requirements for speed and cost reduction but also the softer aspects such as transparency, user-centeredness, fairness, resilience, and trust, that technology alone cannot deliver. The proposed service is designed to streamline claim processes, mitigate implicit bias, and customize procedures based on domain expertise. This integrated approach promises to save time, reduce judicial burdens, and enhance stakeholder satisfaction, underscoring the necessity of aligning service design in dispute resolution.

Disputes between two parties occur in almost all domains such as taxation, insurance, banking, healthcare, etc. The disputes are generally resolved in a specific forum (e.g., consumer court) where facts are presented, points of disagreement are discussed, arguments as well as specific demands of the parties are heard, and finally a human judge resolves the dispute by often favouring one of the two parties. In this paper, we explore the use of large language models (LLMs) as assistants for the human judge to resolve such disputes, as part of our DRAssist system. We focus on disputes from two specific domains -- automobile insurance and domain name disputes. DRAssist identifies certain key structural elements (e.g., facts, aspects or disagreement, arguments) of the disputes and summarizes the unstructured dispute descriptions to produce a structured summary for each dispute. We then explore multiple prompting strategies with multiple LLMs for their ability to assist in resolving the disputes in these domains. In DRAssist, these LLMs are prompted to produce the resolution output at three different levels -- (i) identifying an overall stronger party in a dispute, (ii) decide whether each specific demand of each contesting party can be accepted or not, (iii) evaluate whether each argument by each contesting party is strong or weak. We evaluate the performance of LLMs on all these tasks by comparing them with relevant baselines using suitable evaluation metrics.

Corporate libraries gathering business intelligence have to process an overwhelming stream of business news. Automatic categorization of these news articles based on their primary event (‘NewsType’) can significantly aid in better organization and disbursement. We propose an ensemble of a BiLSTM neural network and a Pre-trained Language Model (PLM) based zero-shot classification approach for solving the categorization task. We also show that fine-tuning the NLI model helps in better accuracy on the task. We evaluate our approaches on two datasets while comparing with multiple baselines.

Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, we address the task of generating audit suggestions that can help auditors to focus their investigations. Specifically, we present NLP techniques to extract hidden knowledge from a corpus of past financial audit reports of many companies, and use it for generating audit suggestions. The extracted knowledge consists of a set of automatically identified sentences containing adverse remarks, the financial variables mentioned in each sentence and automatically assigned XBRL categories for them, since XBRL is a standardized taxonomy in the financial domain. In the absence of suitable labeled data, we adopted a weak supervision approach. We designed a set of high precision linguistic rules to identify adverse remark sentences, created automatically labeled training data using them, and trained BERT-based and other classifiers to identify such sentences. We next presented novel techniques (which are either unsupervised or zero-shot) to assign zero, one, or more XBRL categories to any given adverse remark sentence. We evaluated the proposed approaches, on a large corpus of real-life financial statements and audit reports, against competent baselines. Given a company’s financial statements (already identified as suspicious), and given a subset of financial variables in them that contribute to suspiciousness, we match these with the extracted knowledge base and identify aligned adverse remarks that help the auditor in focusing on specific directions for further investigations.