CONVENTIONAL METHODS
Simplify the problem.
Statistical models and rule-based systems are reliable and interpretable, but they often depend on fixed assumptions, simplified relationships and restricted search spaces.
FintelliQ™ / Quantum–AI Financial Intelligence
Leading the future of finance with Quantum–AI.
FintelliQ™ combines financial-domain expertise with AI and quantum technologies to solve complex challenges across prediction, optimization, risk and anomaly detection.
01 / Why Finance Needs a New Computing Approach
Financial decisions are becoming too complex for conventional technologies alone.
Financial institutions must make faster and more precise decisions across expanding data, variables, scenarios and constraints. Traditional models simplify this complexity. AI learns more sophisticated patterns, but its performance remains dependent on historical data and it does not by itself resolve every optimization, simulation or explainability challenge.
AI
Predict from learned patterns.
AI can identify nonlinear patterns at scale, yet limited or biased data weakens generalization. Prediction alone does not optimize decisions under competing constraints or guarantee sufficient explainability.
QUANTUM–AI HYBRID
Expand the computational options.
Quantum representations, optimization and estimation methods complement AI in complex search spaces, high-dimensional relationships and repeated scenario calculations.
MCKINSEY OUTLOOK · 2035$400–600BPotential economic value of quantum computing in finance
Finance is emerging as one of quantum computing’s largest value-creation opportunities.
McKinsey expects value to come from both improving existing financial processes and enabling new approaches that are difficult to realize with conventional computing alone.
McKinsey finance outlook, 2026 ↗Process improvementFaster and more precise pricing, risk analysis, fraud detection and decision optimization
New capabilitiesNew computational approaches to complex scenarios, correlations and combinatorial search
Hybrid adoptionFinancial institutions can begin testing value without waiting for fully scaled fault-tolerant hardware
02 / FINTELLIQ™ SOLUTION MODULES
Four specialized modules for transforming financial decisions.
FintelliQ™ is one integrated Quantum–AI financial solution composed of four specialized modules. Each module is designed as a deployable product for a defined customer, financial problem and decision output.
EXPLORE FOUR MODULES Scroll to explore · hover to select
01 / CREDIT SCORING
Conceptual representation · not measured financial data
MODULE 01INDUSTRY PoC
Advanced Credit Scoring
Credit bureausBanksFintech
FINANCIAL PROBLEM
Thin-file borrowers · default-risk prediction · alternative-data scoring
COMPUTATIONAL CHALLENGE
Feature relationships become difficult to represent as the number and complexity of variables increase.
METHODS & ALGORITHMS
FINTELLIQ™ APPROACH
Quantum kernels model high-dimensional relationships; XQAI provides interpretable evidence for credit decisions.
DELIVERABLECredit score · risk probability · decision explanation
03 / CORE QUANTUM–AI ALGORITHMS
Algorithms selected for the structure of each financial problem.
FintelliQ™ does not treat quantum computing as a universal replacement for AI. It combines problem formulation, quantum algorithms and AI according to the computational bottleneck that must be solved.
FintelliQ does not apply quantum algorithms indiscriminately. It identifies the computational bottleneck in a financial decision, establishes an AI and conventional baseline, and selectively applies the method that can produce a testable improvement.
LEARNING ALGORITHMCONCEPTUAL MODEL
Quantum Machine Learning
Data encoding changes the representation used to learn relationships.
CORE PRINCIPLE
Maps financial variables into a quantum feature space and learns relationships through quantum kernels or hybrid models.
LIMIT OF AI
Complex high-dimensional relationships become difficult to represent reliably when data is sparse or imbalanced.
FINANCIAL ROLE
Credit classification · default prediction · nonlinear anomaly patterns
MODULE 01 · MODULE 04
04 / APPLICATION PROCESS
From financial problem definition to validated decision application.
PROBLEM → VALIDATED DECISION
A defined decision brief establishes the problem and success criteria.
- 01Define the decision problem
- 02Structure the data
- 03Select and combine algorithms
- 04Compare and validate
- 05Apply to decisions
QUESTION · BASELINE
01
Define the decision problem
Specify the business objective, target decision, variables, constraints, regulatory requirements and measurable success criteria.
QUESTION · BASELINE
02
Structure the data
Prepare financial, transaction, alternative and market data; address sampling, imbalance, time windows and governance requirements.
DATA · FEATURES
03
Select and combine algorithms
Establish conventional and AI baselines, then apply QML, QAOA, QAE, QSVM or XQAI according to the problem structure.
AI · QUANTUM · HYBRID
04
Compare and validate
Measure prediction performance, solution quality, stability, explainability and scalability under matched conditions.
EVIDENCE · VALUE
05
Apply to decisions
Connect validated outputs to credit, portfolio, risk or anomaly-detection workflows and define deployment requirements.
DECISION · DEPLOYMENT
05 / FINTELLIQ™ IN ACTION · NICE INFORMATION SERVICE
Two connected validation tracks with NICE Information Service.
QIC and NICE Information Service are testing alternative-data credit assessment and quantum-optimized lending decisions through two linked PoC tracks.
For individuals and small businesses with limited conventional credit history, the PoC tests whether alternative data and quantum machine learning can provide additional predictive information while preserving comparability and explainability.
PARTNERNICE Information ServiceKorea’s No.1 Credit Information Service Provider
INDUSTRYCredit information & lending
PROJECTTwo connected PoC tracks
DATAPseudonymized / de-identified test data
QUANTUM INFRAPasqal neutral-atom system
Differentiate risk within similar credit scores.
Conventional scores can group borrowers whose underlying risk differs. This PoC tests whether financial and alternative data, re-evaluated with quantum–AI models, can reveal distinctions within those groups. Any improvement must be validated against the classical baseline.
Similar scores → quantum–AI reassessment → finer risk differentiation.Conceptual target, not a measured PoC result. The same nine borrowers are shown before and after.
01Financial & alternative borrower data
02Quantum–AI reassessment + classical baseline
03Within-group risk differentiation
04Validate discrimination & explain the result
REVIEW OUTPUT
Reassessed risk estimatesDiscrimination against the baselineVariable contributions for review
Move from risk prediction to constrained decisions.
AI estimates credit risk. Quantum optimization then searches combinations of lending rules, rates and limits while balancing default risk, approval rate, profitability and operational constraints.
Risk and operating constraints → feasible lending candidates.Illustrative workflow · no borrower records or PoC results shown.
01AI credit-risk prediction
02Objectives & operating constraints
03Quantum optimization search
04Rules · rates · limits candidates
DECISION CANDIDATES
Lending rulesRatesCredit limits
Credit-risk evidenceConstrained lending decisions
Project roles
NICECredit data, domain criteria, established baseline models and industry validation
QICFintelliQ architecture, AI/QML modelling, XQAI and quantum optimization
PasqalNeutral-atom quantum computing infrastructure and development environment for the optimization track
What the PoC evaluates
Credit modelPredictive discrimination · generalization · explainability · baseline comparison
OptimizationSolution quality · computational efficiency · scalability
DeploymentData feasibility · governance · workflow and economic relevance
The section describes the scope and validation design of an ongoing PoC. It does not present unverified performance results. The “first” designation follows NICE’s official description of the project as the first domestic credit-evaluation-industry validation directly applying Quantum–AI to a personal credit model.
FintelliQ™
We are looking for partners to solve complex financial problems together.
QIC works with banks, credit bureaus, card issuers, securities firms, asset managers and other financial institutions to define real business challenges and validate how FintelliQ™ can help address them.
Discuss an Industry PoC →