Operationalizing Generative AI for Retail banking
Subhashini Sharma Tripathi
Data Scientist @ Signify Career Guidance @ CareerTests.in
Operationalizing Generative AI for delivering these services in a retail banking environment in India involves a systematic approach. Here's a practical method to achieve each of the mentioned (refer : https://www.dhirubhai.net/pulse/unlocking-power-generative-ai-retail-banking-youth-sharma-tripathi/ ) Used Cases:
?1. Automated Customer Support
?a. Multilingual Chatbots
1. Data Collection: Gather a comprehensive dataset of customer queries and responses in various languages, including Indian regional languages.
2. Training the Chatbot: Use Generative AI models like GPT-3.5 to train chatbots on this multilingual dataset, fine-tuning them for banking-specific language and context.
3. Integration: Integrate the trained chatbots into your banking platform or website to provide immediate assistance.
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?b. 24/7 Availability
1. Server Infrastructure: Ensure robust server infrastructure to support round-the-clock availability.
2. Load Balancing: Implement load balancing to distribute traffic evenly and prevent server overload.
3. Redundancy: Set up redundancy to ensure system reliability even during server failures.
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?2. Personalized Recommendations
?a. Tailored Product Suggestions
1. Data Collection: Gather customer data, including financial behavior, preferences, and transaction history.
2. AI Model Training: Utilize Generative AI to create personalized product recommendation models based on the collected data.
3. Integration: Integrate these recommendation models into your banking app or website.
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?b. Language-Specific Recommendations
1. Language Preference Capture: Allow customers to specify their language preferences during account setup.
2. Language Mapping: Map language preferences to user profiles and connect them to the recommendation models.
?3. Language-Independent Document Processing
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?a. Loan Application Processing
1. Multilingual Form Recognition: Implement Generative AI models for multilingual form recognition to extract and digitize application data.
2. Translation and Evaluation: Use Generative AI to translate and evaluate the extracted data, ensuring accuracy and completeness.
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?b. Document Verification
1. Document Scanning: Develop a document scanning feature in your banking app that captures documents in any language.
2. Generative AI Verification: Implement Generative AI for document verification, regardless of the language.
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?4. Fraud Detection and Prevention
?a. Multilingual Fraud Analysis
1. Data Collection: Collect transaction data in multiple languages.
2. Generative AI Fraud Models: Train Generative AI models to detect fraud patterns across languages.
3. Real-time Monitoring: Implement real-time monitoring using Generative AI to detect irregularities.
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?b. Real-time Alerts
1. Alert Generation: Develop an alert system that generates alerts in the customer's chosen language.
2. Immediate Response: Ensure that alerts prompt immediate action by the customer or your fraud prevention team.
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?5. Language-Agnostic Marketing
?a. Targeted Campaigns
1. Regional Segmentation: Segment your customer base by region and language preferences.
2. Content Generation: Use Generative AI to create targeted marketing content for each segment.
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?b. Content Localization
1. Generative AI Localization: Employ Generative AI for content localization, adapting messages for linguistic and cultural relevance.
2. A/B Testing: Continuously refine your marketing content based on A/B testing results.
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?6. Multilingual Data Analytics
?a. Customer Insights
1. Data Integration: Integrate data from diverse linguistic sources into a unified analytics platform.
2. Generative AI Analytics: Employ Generative AI for customer behavior analysis, language-independent insights generation, and trend identification.
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?b. Risk Assessment
1. Multilingual Risk Models: Develop risk assessment models that consider linguistic diversity in financial data.
2. AI-driven Credit Scoring: Utilize Generative AI to enhance credit scoring by including linguistic factors.
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?7. Voice Assistants for All
?a. Vocal Transactions
1. Voice Integration: Integrate voice recognition and response systems into your banking app, allowing users to perform transactions through voice commands.
2. Multilingual Voice Models: Train voice models to understand and respond in various Indian languages.
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?b. Accessibility
1. User Training: Offer user training for voice assistants, especially targeting customers with varying tech literacy levels.
2. Accessibility Features: Implement accessibility features like voice-guided menus and commands.
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?8. Legal Compliance
?a. Regulatory Reporting
1. Regulatory Data Collection: Collect regulatory data in multiple languages.
2. Generative AI Reporting: Use Generative AI to automate the preparation of regulatory reports, translating as necessary.
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?b. Contract Generation
1. User Input: Collect user input for contract details.
2. Generative AI Contracts: Employ Generative AI to generate contracts in the user's chosen language.
3. Legal Review: Ensure legal review of contracts to maintain compliance.
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Generative AI has the potential to transform the banking industry by automating routine tasks, enhancing cybersecurity, improving risk management, and personalizing customer experiences. However, operationalizing generative AI for banking services requires a blend of data collection, AI model training, integration, and user-friendly features. It's essential to continuously monitor and update these systems to adapt to evolving customer needs and linguistic preferences. The avenues for further research include identifying the skills, resources, and capabilities needed to handle generative AI and examining biases of generative AI.
Generative AI tools, powered by machine learning models, are key to transforming the retail banking industry and reaching the next set of customers.
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