Transforming Credit & Investment Risk Analysis
Through AI Innovation
Empowering tomorrow’s analysts with advanced technology solutions that transform risk analysis across lending, trading and investing
Deep Domain Expertise
Inia AI is a UK-based startup founded by seasoned credit & investment risk professionals.
Our decades of front-line banking experience ensure solutions that speak directly to real-world challenges.
Our decades of front-line banking experience ensure solutions that speak directly to real-world challenges.
Customised Software Solutions
Workflow driven software solutions to help FIs modernise their credit and investment risk infrastructure. Our platform leverages GenAI to automate risk analysis and risk monitoring.
Analyst-First Approach
We believe in human + AI collaboration. Our platform automates up to 80% of the workload - yet keeps analysts
firmly in control.
firmly in control.
Why Inia AI
Proprietary Environment: Our sophisticated software was rigorously developed and validated within a proprietary, enterprise-grade production environment.
Secure Deployment: Deploys behind firewalls, allowing our clients to maintain full control over data security in compliance with regulatory standards.
Human-in-the-loop: Maintains oversight in decision-making with transparency, interpretability, and continuous feedback, enabling AI to do the heavy lifting while keeping humans firmly in control.
Model Risk Mitigation: Our instruction-based methodology ensures consistent, predictable outputs, every time.
Customised Expert-Backed Workflows: We deliver expert-backed, tailored workflows designed to preserve our clients' unique methodologies and minimise systemic risk.
Introducing
Our Products
Workflow Driven Solutions with End-to-End Co-Pilot Platform
Credit Risk AI-integrated Solutions
Ready-to-Edit Credit Reports: No iterative Q&A or manual rekeying. Analysts upload information, step away and return to a fully developed first draft.
Fully Automated Risk Monitoring: Monthly or quarterly.
Inia Terminal Modules:
a) Funds Credit Risk
b) Corporate Credit Risk, Public & Private
c) Sovereign Credit Risk
a) Funds Credit Risk
b) Corporate Credit Risk, Public & Private
c) Sovereign Credit Risk
The Problem
Financial Data Quality
- Private credit data sources are diverse and often fragmented.
- Little standardization of data across private credit investment portfolios*Inconsistent risk category-name and definition
Financial Data Quality
- Increase consistency and accuracy of ‘illiquid funds’ risk data
- Missed opportunities through delayed insights
- Hedge investment portfolio and capture alpha more effectively
Time-Intensive Requirements
Enhance risk analyst’s capacity to manage workload under pressure
- Private credit data sources are diverse and often fragmented.
- Little standardization of data across private credit investment portfolios*Inconsistent risk category-name and definition
Increase transparency and compliance with regulatory standards
Credit Risk Analysis as a Service
How it Works
Input Data can be structured and unstructured from multiple internal and external sources. Automatic capture of PDFs, spreadsheets, and public web data.
Extract corporate and financial data with precision. We don't simply prompt AI—we harness it through proprietary RAG*, robust fallback methods for exception handling, thorough validations, and rigorous testing to ensure accuracy and consistency.
Standardise financial and corporate metrics sourced from diverse data points into consistent analyses, financial statement spreads, peer comparisons, and industry benchmarks.
Integrate key company insights for comprehensive credit analysis - business overview, management assessment, ownership and operational structure, industry positioning, and financial trends.
Monitor through descriptive and advanced analytics based on data extracted from client reporting. Analysis can be refined to reflect covenants and client thresholds.
Deliver customised credit analysis reports rapidly (~60 minutes/report), significantly reducing workload. Enables analysts to focus more deeply on flagged points and strategic evaluation.
Investment Risk AI-integrated Solutions
Automatically extract and standardise financial data into a unified, consistent format, enabling seamless comparative and pooled analysis across aggregated portfolios without analyst intervention.
Supports real-time risk monitoring, continuously assessing new data against customised limits and generating actionable alerts.
Private Credit Investment Risk Monitoring module.
The Problem
Financial Data Quality
- Private credit data sources are diverse and often fragmented.
- Little standardization of data across private credit investment portfolios*
- Inconsistent data collection, risk category name and definition
Time-Sensitive & Intensive Requirements
- Risk analyst’s capacity and speed to manage workload under pressure
- Improve monitoring the evolution of risk exposures
- Streamline risk escalation process when corrective action is required
- Missed opportunities through delayed insights
- Hedge investment portfolio and capture alpha more effectively
Time-Intensive Requirements
Enhance risk analyst’s capacity to manage workload under pressure
- Improve monitoring the evolution of risk exposures
- Streamline risk escalation process when corrective action is required
Increase transparency and compliance with regulatory standards
How it Works
Investment Risk Monitoring as a Service
Input Data can be structured and unstructured from diverse sources and formats. Automatic capture of PDFs, Excel, emails.
Extract financial data with purpose to increase accuracy. Proprietary RAG is designed by risk analysts in a production environment-fallbacks, and checks through testing.
Standardise financial metrics sourced from funds with diverse risk category definitions and ratio calculations methodology.
Integrate financial metrics across large data sets from private credit funds to enable a comprehensive ‘look through’ across portfolios.
Monitor through descriptive & advanced analytics* based on extracted financial data. Analysis can be refined to reflect client proprietary risk limits and requirements.
Deliver investment risk monitoring reviews. This provides more time to investigate flagged output that require deeper analysis.
*Advanced analytics; scenario analysis, stress testing, custom limits, & written insight are stages 2 & 3 of product development.
Our Commitment to Data Security
Data Confidentiality & Isolation:
- Your data is never used to train AI models.
- Strict isolation ensures AI models cannot access or reuse your information.
Secure Infrastructure Architecture:
- All components reside within your Virtual Private Cloud (VPC), ensuring complete control over data residency.
- AI model requests travel exclusively through encrypted, secure channels
- Data is processed in memory during the request only — no data is stored outside your environment.
- Architecture aligned with SOC 2, ISO 27001, and GDPR standards to satisfy stringent compliance requirements.
Additional Safeguards During Model Interactions:
- Built-in functionality to redact client names for an additional layer of security for highly sensitive information.
- All AI model interactions are fully logged and auditable.
Licensing
We earn your business through delivering measurable results, not contractual obligations.
We've designed a straightforward approach to make evaluation and adoption seamless — offering a proven path with minimal effort and zero upfront investment.
Secure On-Premise Assessment
Risk-Free Trial Period
Transparent Commercial Terms
The Team
Hasan Dajani
Product Lead & Founder
Artem Koloskov
Engineering Lead & Co-Founder
Vince Daphu
Commercial Lead & Co-Founder
Lana Matta
Product Lead, Alternatives & Co-Founder
Nathan Scarrott
AI Solutions Lead
Chris Portelli
CTO Advisory
Turgay Gulmez
Software Lead
Baris Hamil
Software Engineer
Fares Awwad
CFO Advisory
