Enabling AI-Driven Grievance Redressal in Indian Banking with Databricks

Indian banks have witnessed a persistent rise in customer complaints despite significant digital investments. According to the RBI’s Annual Report on the Banking Ombudsman Scheme, customer grievances across Scheduled Commercial Banks (SCBs) rose by over 30% between FY21 and FY23. The top three areas of concern include:

• Unauthorized electronic transactions
• Delays or failure in service delivery
• Issues in digital payment platforms and mobile banking

Public sector banks (PSBs), while improving, still lead in the absolute number of complaints, whereas private and small finance banks face criticism for service lapses in high-growth digital channels. Additionally, customers increasingly voice dissatisfaction through social media, emails, IVR logs, and multilingual chat systems, creating a flood of unstructured data difficult to manage with legacy complaint systems.

The RBI’s AI Mandate: A Shift from Reactive to Predictive Service:

Recognizing this, the Reserve Bank of India (RBI), under Governor Shaktikanta Das, has urged banks and NBFCs to embed Artificial Intelligence (AI) into their grievance redressal frameworks. The objective is twofold: reduce resolution times and enhance the quality of customer service by intelligently leveraging data. The RBI also emphasizes fairness, explainability, and multilingual support—elements often overlooked in traditional automation efforts.

Databricks: A Unified Platform to Accelerate AI-First Grievance Redressal:

Databricks provides a powerful foundation for this transformation with its Lakehouse architecture and an expanding suite of AI capabilities including DBRX (LLM), Mosaic AI, ML Ops, Clean Rooms, and the AI BI Genie.

1. DBRX – Advanced Language Intelligence for Complaint Understanding:

DBRX, Databricks’ state-of-the-art open-source Large Language Model (LLM), helps banks unlock insights from unstructured grievance data. Key applications include:
• Parsing customer complaints from IVR transcripts, emails, WhatsApp chats, and app reviews
• Grievance classification, sentiment scoring, and urgency prediction
• Generative responses for first-level support or follow-up communication

Example Code (in Databricks Notebook):

python
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from transformers import pipeline
nlp_pipeline = pipeline(“text-classification”, model=”databricks/dbrx-base”)
sample_text = “I have been charged twice for the same transaction. Please fix this.”
response = nlp_pipeline(sample_text)
print(response)

This model can be fine-tuned on Indian banking-specific complaints and integrated into automated escalation engines.

2. Mosaic AI – Banking-Ready GenAI Workflows with Multilingual Capabilities:

Mosaic AI enables the seamless deployment of multilingual AI agents and dynamic workflows built for banking-specific use cases.

Capabilities:

Hindi, Kannada, Tamil, Bengali language support via fine-tuned DBRX
GenAI chatbots that autonomously resolve 70–80% of recurring grievances
Retrieval-Augmented Generation (RAG) systems powered by Databricks Vector Search

Workflow Steps:

Fine-tune DBRX using complaint histories in regional languages.
Build a RAG system using LangChain and Databricks-native vector embeddings.
Deploy GenAI support assistants via Model Serving.

3. ML Ops – Responsible AI at Scale:

Operationalizing AI across a bank’s support operations demands rigorous monitoring and governance. Databricks’ ML Ops capabilities ensure:

Model lifecycle tracking with MLflow
Bias and drift detection aligned with RBI’s ethical AI guidelines
Auto-retraining pipelines based on concept drift or regulatory thresholds

Example MLflow Logging:

python
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import mlflow
with mlflow.start_run():
    mlflow.log_param("model_type", "xgboost")
    mlflow.log_metric("accuracy", 0.94)
    mlflow.sklearn.log_model(model, "grievance_classifier")

4. AI BI Genie – Augmented Analytics for Customer Service Teams

Databricks’ AI BI Genie acts as a conversational analytics layer over Delta tables and dashboards.

Enables service leaders to query data in natural language (e.g., “Show me unresolved complaints in rural branches last month”)
Helps agents spot patterns in customer sentiment or service delays
Auto-suggests action items based on recurring service failures or SLA breaches

This promotes data democratization and real-time insights without needing to write complex SQL.

5. Clean Rooms – Privacy-Preserving Complaint Pattern Benchmarking

For banks looking to benchmark their complaint volumes and patterns while preserving customer privacy, Databricks Clean Rooms provides:

A secure, collaborative data-sharing layer
Ability to compare complaint patterns with peer institutions or fintech partners without exposing PII
Regulatory compliance for multi-entity grievance insights (e.g., NBFC–Bank partnerships)

Use Cases:

Cross-bank grievance benchmarking
Evaluating grievance reduction impact post-branch digitization
Real-time fraud pattern correlation using anonymized datasets

6. Unified Lakehouse Analytics – The Foundation for Scalable Grievance Redressal

The Lakehouse architecture allows banks to consolidate structured (CBS, CRM) and unstructured (emails, chat logs, audio) data. This enables:

A 360° view of each customer’s grievances and touchpoints
Complaint prioritization based on urgency, sentiment, and financial exposure
Workflow integration with ServiceNow, Salesforce, or in-house CRM tools

Sample Data Flow:

python
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raw_df = spark.read.format("csv").option("header", True).load("/mnt/inbox/grievances.csv")
transformed_df = raw_df.withColumn("sentiment", sentiment_udf(col("complaint_text")))
transformed_df.write.format("delta").saveAsTable("bank_complaints_transformed")

7. Governance and Trust – Aligning with RBI’s Ethical AI Guidelines:

Databricks supports ethical AI through:

Unity Catalog for audit logging, data lineage, and access control
Explainability tooling for model decision transparency
Change Data Feed for historical audit trails

Governance Action Snippet:

sql
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-- Enable data lineage
ALTER TABLE bank_complaints_transformed SET TBLPROPERTIES (
  'delta.enableChangeDataFeed' = 'true'
);

8. Solution Blueprint Diagram (Figure 1)

The solution architecture includes:

Ingestion from core banking, CRM, IVR, social media
Natural language processing with DBRX
GenAI chatbot workflows using Mosaic AI
BI dashboards powered by AI BI Genie
Privacy-preserving analytics with Clean Rooms
Governance via Unity Catalog and MLflow

Summary: Redefining Customer Service with Databricks:

India’s banking sector is at an inflection point. Customer expectations are rising, and regulatory oversight is intensifying. The RBI’s call for AI-driven grievance redressal isn’t just a compliance mandate—it’s an opportunity to transform how banks serve millions of Indians across languages, geographies, and digital channels.

Databricks delivers an end-to-end platform to make this vision real. By integrating DBRX, Mosaic AI, ML Ops, Clean Rooms, and AI BI Genie, banks can:

Resolve complaints faster and more fairly
Reduce operational costs via automation
Maintain full regulatory compliance
Build lasting trust with their customers

This is not just an AI initiative. It’s a shift towards intelligent, inclusive, and insight-led banking.

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Enabling AI-Driven Grievance Redressal in Indian Banking with Databricks