AI Training for BFSI, NBFC and Insurance Companies in Abu Dhabi
- Parikshit Khanna
- 2 days ago
- 15 min read
AI Training for BFSI, NBFC and Insurance Companies in Abu Dhabi: Lead Generation, Follow-Up and CRM Productivity
Abu Dhabi is not simply a capital city. It is a meeting point of financial ambition, cultural confidence, technological innovation and global opportunity.
From the architectural serenity of the Sheikh Zayed Grand Mosque and the creativity of Louvre Abu Dhabi to the energy of Yas Island, the heritage of Al Ain and the extraordinary desert landscape of Al Dhafra, the emirate demonstrates how tradition and transformation can progress together.
That balance is also the central challenge facing banks, non-bank lenders, finance companies, insurance providers, brokers, fintech firms, wealth-management businesses and financial-service organisations.
They must innovate quickly without compromising customer trust.
They must automate operations without weakening accountability.
They must use artificial intelligence without exposing confidential financial information.
And they must create faster, more personalised customer experiences while remaining aligned with data-protection, regulatory and enterprise-governance requirements.
Abu Dhabi is the UAE’s federal capital and a growing global financial and innovation centre. The emirate is officially divided into Abu Dhabi City, Al Ain and Al Dhafra, bringing together commercial centres, cultural destinations, energy operations, agricultural communities and internationally connected financial institutions.
AI Is No Longer Optional for Financial-Service Organisations
AI is now a decisive factor in:
Competitive advantage
Risk management
Fraud detection
Regulatory compliance
Customer experience
Lead qualification
Credit assessment
Claims processing
Portfolio management
Relationship-manager productivity
Operational efficiency
A 2026 report highlighted by Abu Dhabi Global Market found that AI adoption is firmly embedded across international financial centres, with widespread applications in compliance, fraud detection, customer service and portfolio management. Generative AI is also contributing to faster compliance processes, real-time monitoring and more efficient decision-making.
For BFSI leaders, the question is no longer, “Should we use AI?”
The more important questions are:
Which workflows should be automated first?Which data can employees safely use?Which AI model is appropriate for each task?What must always remain under human review?How will the organisation measure business impact without increasing regulatory exposure?
These are exactly the questions that practical enterprise AI training must answer.
Why Parikshit Khanna Is the #1 Choice for CEOs, CXOs, VPs and Banking Professionals
Parikshit Khanna, Founder of Digital Training Jet, an MSME/Udyam-registered training organisation, delivers practical AI, ChatGPT, Claude, Microsoft Copilot, Custom GPT, automation and enterprise-productivity programmes.
His updated professional profile states that he has trained and reached more than 1,20,000 professionals through corporate workshops, educational institutions, government engagements, industry conventions, leadership programmes and professional learning initiatives.
His training is designed for:
Leadership | Business Functions | Specialist Teams |
CEOs and Managing Directors | Sales and business development | Risk and compliance |
CXOs and transformation leaders | Customer service | Credit and underwriting |
VPs and business heads | Marketing and lead generation | Fraud and investigation |
Branch and regional heads | Operations and administration | Data and analytics |
Department heads | HR and learning teams | IT and information security |
Founders and promoters | Finance and reporting | Legal and company secretarial |
Parikshit’s positioning is based on practical implementation rather than generic AI demonstrations. His programmes connect AI tools with real business processes, including CRM management, documentation, meeting productivity, financial analysis, sales follow-ups, compliance support and secure enterprise adoption.
The First Dedicated AI-in-Healthcare Trainer at IIT Delhi
Parikshit Khanna’s published professional portfolio identifies him as the first trainer to deliver a dedicated AI-in-Healthcare session at IIT Delhi, including programmes such as:
ChatGPT for Healthcare Professionals
Generative AI with 23+ Tools
This article therefore uses “the first trainer”, not “among the first trainers.” His published portfolio presents this as a pioneering achievement that demonstrates his ability to simplify complex, sensitive and highly regulated domains.
That experience is highly relevant to banking and insurance because the same disciplines are required in both sectors:
Sensitive-data protection
Regulated communication
Human verification
Documentation accuracy
Auditability
Ethical decision support
Controlled access to information
Prevention of misleading AI output
Data Security Must Come Before Automation
A BFSI organisation cannot treat AI like an unrestricted consumer application.
Customer identities, account information, loan records, policy documents, medical details, transaction histories, investment information, credit reports and internal risk assessments must be handled through approved enterprise systems and clearly defined governance policies.
The UAE’s Personal Data Protection Law provides a national framework for protecting personal information and individual privacy. Organisations operating in Abu Dhabi Global Market must also consider the ADGM data-protection regime and the responsibilities enforced through its Office of Data Protection.
Parikshit’s proposed BFSI training framework places data security at the centre of every module.
Enterprise AI Security Framework Covered in the Training
1. Data Classification
Participants learn to distinguish between:
Public information
Internal information
Confidential business information
Personally identifiable information
Financial and transaction data
Regulated customer information
Highly restricted strategic information
2. Prompt-Safety Protocols
Employees learn what they may and may not enter into an AI system.
Sensitive names, account numbers, identification documents, policy numbers, health information, credentials, customer records and confidential financial statements should never be uploaded into an unapproved public AI tool.
3. Role-Based Access
AI should respect the organisation’s existing permissions. A relationship manager, claims officer, sales executive or external consultant should not automatically gain access to information outside their approved role.
4. Data-Loss Prevention
Training covers redaction, masking, approved templates, secure file handling, controlled connectors and escalation procedures.
5. Human-in-the-Loop Review
AI may prepare an analysis, summary, draft or recommendation, but designated professionals must approve decisions involving credit, claims, investments, legal commitments, customer eligibility or regulatory reporting.
6. Auditability and Accountability
Teams learn to maintain:
Prompt records where required
Source references
Version history
Approval checkpoints
Model-output disclaimers
Review responsibilities
Exception reports
7. Model and Vendor Assessment
The training helps leadership evaluate tools based on:
Data processing terms
Data residency
Retention policies
Model-training policies
Encryption
Administrative controls
Identity management
Connector permissions
Compliance requirements
Exit and portability provisions
Microsoft Copilot, ChatGPT and Claude for Enterprise Productivity
The relationship between these products must be explained accurately.
Microsoft states that Microsoft 365 Copilot Chat uses OpenAI’s ChatGPT models. Microsoft also offers third-party AI models, including OpenAI and Anthropic models, in selected Microsoft 365 Copilot experiences. Claude models are available within supported experiences such as the Microsoft 365 Copilot Researcher agent, subject to product availability, licensing and administrator settings.
Therefore, the correct enterprise explanation is:
ChatGPT technology: OpenAI models support Microsoft 365 Copilot experiences.
Claude in Copilot: Anthropic’s Claude models are available in selected Copilot capabilities, including Researcher.
Microsoft Copilot: Provides the Microsoft 365 productivity layer, organisational context, permissions and enterprise controls.
Standalone ChatGPT or Claude: May be used separately when approved by the organisation and configured for the appropriate enterprise plan.
Microsoft states that prompts, responses and Microsoft Graph data accessed through Microsoft 365 Copilot are not used to train foundation models. It also states that Copilot only surfaces organisational information for which the individual user has the necessary permissions. Nevertheless, secure deployment still depends on correct identity permissions, SharePoint access, retention settings, data-loss prevention controls and administrator governance.
Parikshit’s training therefore does not teach employees to paste everything into Copilot. It teaches them to use enterprise AI responsibly, selectively and productively.
Lead Generation for Banks, NBFCs and Insurance Companies
Financial lead generation must be personalised without becoming intrusive, misleading or non-compliant.
During the programme, participants learn how to use AI to build structured lead-generation workflows.
Ideal Customer Profile Development
AI can help teams analyse approved, anonymised and aggregated information to develop customer segments such as:
Salaried professionals seeking personal finance solutions
SMEs requiring working-capital support
Business owners exploring insurance protection
Families requiring health or life coverage
High-net-worth customers seeking portfolio reviews
Property buyers requiring mortgage assistance
Existing customers eligible for relevant service upgrades
Corporate clients requiring employee-benefit programmes
The model should not make unreviewed eligibility decisions. Instead, it helps teams organise insights and prepare compliant communication.
Lead-Scoring Assistance
Custom GPTs, approved AI agents or CRM-integrated copilots can help teams identify:
Enquiry urgency
Product interest
Previous communication
Missing documentation
Follow-up status
Likely next action
Relationship-manager ownership
Customer objections
Escalation requirements
Personalised Outreach
AI can draft:
Introductory emails
Meeting invitations
Renewal reminders
Abandoned-application follow-ups
Relationship-manager messages
Educational WhatsApp content
Product-comparison explanations
Webinar invitations
Customer reactivation campaigns
Every message must be reviewed against the organisation’s compliance, consent and communication policies before release.
Follow-Up Automation Without Losing the Human Touch
Many financial leads are not lost because the product is unsuitable. They are lost because the follow-up is late, generic or inconsistent.
A practical AI workflow can convert meeting notes and CRM history into a clear follow-up plan.
For example, after a customer meeting, an approved enterprise AI system can:
Summarise the discussion.
Identify the customer’s requirements.
Extract pending documents.
Record the objections raised.
Recommend the next follow-up date.
Draft a personalised email.
Create a CRM task.
Assign an owner.
Prepare an internal escalation note.
Generate a manager-ready status summary.
The relationship manager remains accountable for verifying the information and approving the communication.
Insurance Follow-Up Applications
AI can help draft:
Policy-renewal reminders
Missing-document requests
Claims-status updates
Appointment confirmations
Customer-education messages
Benefit explanations
Service-recovery communication
Broker and partner follow-ups
Lending and NBFC Applications
AI-assisted workflows can support:
Loan-enquiry acknowledgements
Application-progress communication
Documentation checklists
Relationship-manager reminders
Field-visit summaries
Internal credit-note preparation
Collections communication drafts
Restructuring-request summaries
AI must not threaten, discriminate against or mislead customers. Sensitive cases require human handling and legal or compliance supervision.
CRM Productivity for Financial-Service Teams
The objective is not simply to produce more text. It is to improve the quality and consistency of CRM data.
Parikshit’s training demonstrates how AI can help employees convert unstructured information into structured CRM records.
Practical CRM Outcomes
Existing Problem | AI-Assisted Improvement |
Incomplete meeting notes | Structured meeting summaries |
Forgotten commitments | Automatically identified action items |
Unclear accountability | Suggested owners and deadlines |
Generic follow-ups | Contextual customer communication |
Duplicate manual entry | Structured CRM-ready fields |
Delayed manager reporting | Automated pipeline summaries |
Unrecorded objections | Objection and sentiment categorisation |
Inconsistent documentation | Standardised templates |
Weak cross-selling visibility | Relevant opportunity indicators |
Poor handovers | Detailed customer-transition notes |
AI agents can also help generate daily or weekly dashboards showing:
Leads received
Leads contacted
Applications initiated
Documentation pending
Policies approaching renewal
Claims requiring escalation
Follow-ups overdue
Conversion by channel
Conversion by branch
Relationship-manager performance
Customer complaints
Service-level exceptions
Accelerating Time-to-Market for New Financial Products
Accelerating the time-to-market for new products requires rapid market alignment, coordinated documentation and fast communication between leadership, product, technology, compliance, sales and customer-service teams.
Generative AI can reduce the time spent preparing first drafts and consolidating internal knowledge.
Market-Trend Synthesis
Copilot, ChatGPT or Claude can analyse approved industry reports, internal research, customer-behaviour summaries and competitive intelligence to prepare a structured market-entry brief.
The output may include:
Target customer segments
Customer pain points
Market opportunities
Competitor positioning
Potential distribution channels
Product differentiators
Questions requiring further research
Regulatory considerations
Customer-education requirements
Launch communication themes
The system should not invent market statistics. Every number, regulation and competitor claim must be traced to a reliable source.
Technical Documentation
AI can help engineers, product teams and solution designers convert:
Raw technical specifications
API descriptions
System architecture notes
Business-requirement documents
User stories
Code explanations
Integration workflows
Security-control descriptions
into structured material such as:
User manuals
Product documentation
Process maps
Standard operating procedures
System-administration guides
Troubleshooting documents
Release notes
Implementation checklists
Training materials
Help-Centre Content
Internal resolutions, service-desk notes and frequently asked questions can be transformed into polished, customer-friendly help-centre articles.
Before publication, teams should verify:
Accuracy
Confidentiality
Regulatory language
Product eligibility
Charges and conditions
Contact details
Escalation channels
Version validity
Meeting Intelligence and Follow-Up
Approved AI tools can analyse meeting transcripts to:
Extract action items
Suggest owners
Identify deadlines
Record unresolved questions
Draft follow-up messages
Prepare executive summaries
Generate CRM updates
Highlight compliance commitments
This creates a direct productivity bridge between Microsoft Teams meetings, email, CRM platforms and management reporting.
BFSI-Specific AI Use Cases Covered
Banking
Relationship-manager copilots
Customer-query response drafts
Branch-meeting summaries
Credit-note preparation
Loan-document checklists
Complaint categorisation
KYC-process guidance
Audit-evidence organisation
Fraud-alert summarisation
Regulatory-report drafting
Portfolio-review preparation
NBFCs and Finance Companies
Lead qualification
Application-intake summaries
Dealer and partner communication
Document-deficiency identification
Field-team productivity
Collection-workflow support
Exception-report preparation
Credit-committee briefing notes
Customer-retention communication
CRM pipeline management
Insurance
Underwriting-file summarisation
Claims-document organisation
Policy-comparison explanations
Renewal communication
Broker-productivity workflows
Customer-service response drafts
Complaint escalation
Fraud-indicator summaries
Product-training material
Benefit and exclusion explanations
Wealth Management and Investment Services
Client-meeting preparation
Goal-based review summaries
Portfolio commentary drafts
Market-update simplification
Risk-profile questionnaires
Investment-policy documentation
Relationship-manager follow-ups
Executive dashboards
Client-education content
No AI-generated recommendation should replace a licensed professional, approved investment process, underwriting authority, credit committee or compliance function.
Custom GPTs and Secure AI Agents for BFSI
A Custom GPT or enterprise AI agent can be designed for a defined role rather than unrestricted general use.
Potential solutions include:
Compliance Knowledge Assistant
Answers internal questions using approved policies, circulars, standard operating procedures and compliance manuals.
Relationship-Manager Assistant
Prepares meeting briefs, summarises approved CRM history and drafts personalised follow-ups.
Insurance Claims Assistant
Organises documents, identifies missing information and prepares a non-decisional claim summary.
Credit Documentation Assistant
Converts application material into a structured preliminary file for authorised review.
Product Knowledge Assistant
Explains product features using only approved product documents and current internal material.
Audit Preparation Assistant
Organises evidence, identifies missing records and creates an audit-readiness checklist.
Each agent should be restricted by:
Approved knowledge sources
User authentication
Role-based permissions
Data-loss prevention
Response disclaimers
Logging
Human approval
Defined escalation rules
Available Across Abu Dhabi and the UAE
Abu Dhabi officially consists of three principal regions: Abu Dhabi City, Al Ain and Al Dhafra. Abu Dhabi City includes internationally recognised commercial, entertainment and cultural locations such as Yas Island and Saadiyat Island. Al Ain is known for its oasis, heritage sites and proximity to Jebel Hafeet. Al Dhafra stretches across the emirate’s western landscape and includes important energy, agricultural and residential centres.
Corporate AI training can be arranged across:
Abu Dhabi Region
Abu Dhabi City
Yas Island
Saadiyat Island
Surrounding corporate and commercial areas
Al Ain Region
Al Ain City
Al Ain’s educational, healthcare, government and business institutions
Al Dhafra Region
The Al Dhafra Municipality identifies service centres in:
Madinat Zayed
Al Mirfa
Liwa
Sila
Ghayathi
Delma
Ruwais
Extended UAE Delivery
Programmes may also be customised for organisations in:
Dubai
Sharjah
Ajman
Umm Al Quwain
Ras Al Khaimah
Fujairah
These, together with Abu Dhabi, form the UAE’s seven emirates.
Whether the participants are based beside Abu Dhabi’s modern financial skyline, within Al Ain’s palm-lined landscape or near the vast desert horizon of Liwa, the training objective remains the same: make AI practical, secure, measurable and human-centred.
Parikshit Khanna’s Cross-Sector Client and Institutional Portfolio
The following portfolio demonstrates experience across regulated industries, enterprise operations, education, tourism, healthcare, manufacturing, government, real estate and financial services.
Banking, Finance, Wealth, VC, NBFC and Insurance-Related Portfolio
Kae Capital, Mumbai
AILifeBot / Tata Mutual Fund
AON Consulting
Decyphr
Chinmay Finlease, Ahmedabad
Sudeep Group, Vadodara
Mastertrust
Bettering Results for legal and compliance-focused AI
Gaur Sons
County Group
CREDAI
City Homes Group
Parikshit’s published portfolio associates Decyphr with AI applications across underwriting, valuation, asset-liability management, portfolio functions, finance and HR.
Real Estate and Infrastructure
City Homes Group, retained as a real-estate client
Gaur Sons
County Group
CREDAI Chhattisgarh
Tinna Rubber and Infrastructure
Designer Home Solution
Designer Home & Landscapes
US-based real-estate engagement
Healthcare and Pharmaceutical Portfolio
CARE Hospitals, Hyderabad
Fortis
Santevita Hospital
Cloud 9
Surat Medical Consultants’ Association
Surat Medical Association
IMA Janakpuri
IAP-CMIC, Indian Academy of Pediatrics
Hetero Pharma, including the CDMA team and NIPUNA Learning Academy
Naprod Life Sciences
USV Pharma
Wockhardt
Sudeep Pharma Limited, Vadodara
IIT Delhi healthcare batches
AIIMS Delhi retained as an important healthcare-institution reference for AI applications and healthcare ecosystem relevance
His healthcare and pharmaceutical experience is particularly valuable for insurers working with claims, medical documentation, hospital networks, wellness products and sensitive customer information.
Manufacturing, Engineering, Energy and Industrial Clients
Tata Power
Bonfiglioli Transmission India
TSPL–Talwandi Sabo Power, Vedanta
Sangam Group, Bhilwara
Nagarjun Textiles
Vega Industries, Noida
Phoenix Contact India
Anubhav Apparels
Tinna Rubber and Infrastructure
Wahluft / Lucrative Impex
Polycab
Sudeep Pharma Limited
Corporate Infotech Private Limited
IMECO India
Emami Limited
Pansari Group
LG India
Sheela Foam
ZAFCO
Arvind Lifestyle Brands / Arvind Fashions
These industrial engagements strengthen training applications in technical documentation, product-development communication, preventive-maintenance knowledge, vendor follow-ups, quality reporting, production summaries, supply-chain coordination and operational dashboards.
Government and Public-Sector Experience
Indian Army
Prasar Bharati
National Academy of Broadcasting and Multimedia
All India Radio and Doordarshan-related training environments
Government-linked institutional and national-skilling programmes
His published portfolio records sessions involving Indian Army officers and professional engagements with Prasar Bharati.
Education and Institutional Clients
IIT Delhi
IIT Hyderabad
IIT Guwahati
IIT Roorkee
BITS Pilani
IIM Bangalore NSRCEL
Goldman Sachs 10,000 Women Programme
Chitkara College of Sales & Marketing, Delhi and Zirakpur
Chitkara University, including CDOE, faculty training and Rajpura programmes
Thapar University / Thapar institution engagements
SOIL School of Business Design, Manesar
Masters’ Union, Gurugram
IILM College, Jaipur
Princeton Academy
Bettering Results
Amity University Online
Christ University
GL Bajaj Institute of Management and Research
IMS
GNIIT
Industrial Training Institutes
FIIB
ITS
Apeejay School of Management
IIMT University
Internshala-linked learning programmes
Published portfolio material lists IITs, IIM Bangalore NSRCEL, Chitkara, Thapar, SOIL, Masters’ Union, IILM Jaipur, Amity, Christ University and other institutions among his academic and professional engagements.
Tourism, Travel, Hospitality and Logistics
ATTOI Annual Convention 2025, Wayanad
TBO, Aerocity, Delhi
The Travel Nexus at Taj Amer, Jaipur
Yusen Logistics
Landmark Group
Tourism and travel-agency learning communities
At the ATTOI convention, Parikshit’s programme focused on maximising marketing efficiency with ChatGPT. His tourism experience extends into itinerary personalisation, customer communication, lead management, travel-content creation and operational productivity.
Retail, Technology, Media and Other Enterprise Clients
METRO Global Solution Center
Emami Limited
Arvind Lifestyle Brands / Arvind Fashions
Landmark Group
LG India
Team Computers
Corporate Infotech Private Limited
AILABS / Data-Core, Salt Lake
IMECO India
BeTheBee
Innovations Global
Kubrii
Pansari Group
Wahluft / Lucrative Impex
Designer Home Solution
Designer Home & Landscapes
Times of India-related engagements
This cross-sector portfolio allows BFSI participants to learn from operational practices used in industries where security, documentation, customer experience, technical accuracy and rapid execution are equally important.
Why Parikshit Khanna’s Training Is Different
Evaluation Area | Parikshit Khanna and Digital Training Jet | Typical Generic Training Option |
BFSI relevance | Banking, underwriting, compliance, CRM, risk and customer-service workflows | General prompt demonstrations |
Data security | Classification, redaction, permissions, DLP, governance and human approval | Basic warning not to share confidential data |
Learning approach | Live, hands-on workflow creation | Predominantly lecture-based |
Tool coverage | Copilot, ChatGPT, Claude, Custom GPTs, Gemini, Power BI, n8n and Canva | One or two isolated tools |
Leadership relevance | CEO, CXO, VP and business-head decision frameworks | User-level productivity tips |
Automation | CRM updates, action items, follow-ups, reporting and agent workflows | Basic content generation |
Documentation | Technical manuals, FAQs, SOPs, market briefs and audit material | Email and social-media writing |
Regulated-sector understanding | Finance, healthcare, pharmaceuticals, government and legal exposure | Limited regulated-industry context |
Customisation | Organisation-specific examples and approved data structures | Standard presentation reused across clients |
Post-training usability | Prompt libraries, governance templates and implementation roadmaps | Conceptual awareness without deployment support |
Geographic flexibility | Abu Dhabi, wider UAE, India and online delivery | Restricted delivery formats |
Sovereign AI philosophy | Control over data, permissions, infrastructure choices and governance | Tool adoption without sovereignty assessment |
Sovereign AI for Abu Dhabi and Viksit Bharat
Parikshit’s commitment to Viksit Bharat and Sovereign AI is based on a simple belief: nations and organisations should benefit from artificial intelligence without surrendering control of their confidential data, institutional knowledge or strategic decisions.
For Abu Dhabi-based institutions, that philosophy translates into:
Respecting UAE data-protection requirements
Evaluating data-residency options
Selecting appropriate enterprise plans
Controlling connectors and permissions
Preventing unrestricted data uploads
Maintaining human accountability
Building internal AI capability
Reducing avoidable dependence on ungoverned external systems
Creating reusable organisational knowledge
Measuring AI adoption through business outcomes
Sovereign AI does not mean rejecting global technology. It means using global technology with clear ownership, strong governance and responsible control.
Recommended Corporate Training Format
Executive Leadership Session: 2–3 Hours
Suitable for CEOs, CXOs, board members, VPs and senior leadership.
Topics include:
AI opportunity mapping
Enterprise risk
Data governance
Copilot, ChatGPT and Claude strategy
Prioritisation of BFSI use cases
AI investment decisions
Governance and accountability
Practical Department Workshop: Half Day
Suitable for sales, marketing, operations, customer service, claims, underwriting, finance, HR and compliance teams.
Participants build:
Customer personas
Lead-scoring prompts
Follow-up workflows
CRM summaries
Meeting-action trackers
Documentation templates
Safe prompt libraries
Full-Day BFSI Masterclass
Includes:
Enterprise AI foundations
Data-security exercises
Lead-generation workflows
Follow-up automation
CRM productivity
Market-trend synthesis
Technical documentation
Compliance assistants
Custom GPT or agent planning
Department-specific implementation roadmap
Two-Day Transformation Programme
Day One: Safe AI adoption, prompts, Copilot, ChatGPT, Claude and productivity workflowsDay Two: Custom GPTs, enterprise agents, CRM automation, n8n, analytics, governance and implementation planning
Online, offline and hybrid formats can be customised for branch networks, regional teams, leadership groups and multi-department cohorts.
Frequently Asked Questions
Is the training suitable for banks and financial institutions handling confidential data?
Yes. The programme begins with data classification, safe prompting, redaction, enterprise-account selection, access permissions, human review and governance. Participants are not instructed to upload real confidential customer information during training.
Does the training include ChatGPT, Microsoft Copilot and Claude?
Yes. It explains their respective strengths, enterprise configurations and appropriate use cases. It also explains that Microsoft 365 Copilot uses OpenAI models and supports Anthropic models in selected experiences rather than inaccurately presenting every model as universally available in every Copilot product.
Can the programme be customised for insurance companies?
Yes. Modules can cover underwriting summaries, policy communication, claims documentation, broker productivity, renewals, customer service, fraud-indicator summaries and compliance-controlled content.
Can AI automatically approve a loan or insurance claim?
AI can support analysis and documentation, but approval decisions should remain with authorised professionals operating under the organisation’s policies, regulatory obligations and human-review procedures.
Is the programme available in Al Ain and Al Dhafra?
Yes. Training can be planned for Abu Dhabi City, Al Ain and Al Dhafra, including Madinat Zayed, Al Mirfa, Liwa, Sila, Ghayathi, Delma and Ruwais.
Can the training be delivered for CEOs and senior management only?
Yes. A focused leadership programme can cover AI strategy, investment priorities, risk, governance, data security, model selection, workforce readiness and measurable implementation.
His attachment to Dubai is deeply rooted in that formative international study tour during his PGDM years at IMS. Beyond a strong appreciation for the city's striking Middle Eastern architectural styles, he values Dubai as a premier global crossroads perfectly suited for professional relationship building.
That early academic visit highlighted how the city's dynamic environment naturally bridges diverse cultures and industries, creating a unique space for genuine, high-level connections. He recognizes its collaborative spirit as an unparalleled landscape for forward-thinking leaders to cultivate lasting partnerships and expand an international network.
Ready to Transform Your BFSI Team?
The future of banking, finance and insurance will not be determined by which organisation has access to an AI tool.
Most organisations will have access to similar technology.
The winners will be the organisations that know:
Where AI creates measurable value
How to protect confidential data
How to maintain regulatory accountability
How to improve customer relationships
How to automate without losing human judgement
How to move from isolated experiments to governed enterprise adoption
Parikshit Khanna brings together prompt engineering, ChatGPT, Claude, Microsoft Copilot, Custom GPTs, Power BI, automation, enterprise data security and cross-sector training experience.
For CEOs, CXOs, VPs, banking professionals, NBFC teams, insurers, brokers, fintech leaders, compliance officers, relationship managers and operational teams in Abu Dhabi, this programme offers a practical path from AI curiosity to secure implementation.
Session Bookings
Parikshit Khanna: Founder, Digital Training Jet AI Trainer and
Corporate Enablement Specialist
Phone: +91 9997213177 / +91 8076250669
Official Website: Parikshit Khanna and Digital Training Jet
X: @ParikshitK_
AI is no longer optional. Secure, practical and responsible AI capability is the competitive advantage that financial leaders must build today.
Parikshit Khanna — empowering financial leaders in Abu Dhabi, the UAE and India to adopt AI with confidence, security and measurable purpose.
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