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.
4 Solution Modules
Quantum-AI Algorithms
NICE Credit PoC
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™ Problem-Solving Process
Start with the financial problem. Apply the right combination of intelligence.
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.
01
Define the decision
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 & combine methods
Use conventional models and AI as baselines, then apply QML, quantum optimization, estimation or XQAI where the problem structure supports a clear hypothesis.
AI · QUANTUM · HYBRID
04
Validate decision value
Compare predictive performance or solution quality under matched conditions and assess stability, explainability, scalability and operational relevance.
EVIDENCE · VALUE
Pattern & representation
Tests alternative feature representations for classification, prediction and anomaly detection.
Combinatorial decisions
Searches decision combinations across objectives, rules and operating constraints.
Estimation & scenarios
Explores quantum methods for repeated sampling, valuation and risk estimation.
Explanation & governance
Connects model output to interpretable drivers, diagnostics and validation evidence.
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.
MODULE 01INDUSTRY PoC
Advanced Credit Scoring
신용평가 고도화
Credit bureausBanksFintech
FINANCIAL PROBLEM
Thin-file borrowers · default-risk prediction · alternative-data scoring
AI LIMIT
Feature relationships become difficult to represent as the number and complexity of variables increase.
QUANTUM–AI ALGORITHMS
FINTELLIQ™ APPROACH
Quantum kernels model high-dimensional relationships; XQAI provides interpretable evidence for credit decisions.
DELIVERABLECredit score · risk probability · decision explanation
MODULE 02OPTIMIZATION
Portfolio Optimization
포트폴리오 최적화
Asset managersPension fundsBanks
FINANCIAL PROBLEM
Asset allocation · rebalancing · ruleset decisions under multiple constraints
AI LIMIT
The search space expands combinatorially, making global optimality difficult to secure.
QUANTUM–AI ALGORITHMS
FINTELLIQ™ APPROACH
Encodes objectives and constraints together and searches large scenario spaces for high-quality combinations.
DELIVERABLEOptimized portfolio · rebalancing scenario · ruleset candidate
MODULE 03RISK
Risk Assessment
금융 리스크 평가
Investment banksInsurersSecurities firms
FINANCIAL PROBLEM
CVA/XVA · derivatives risk · exposure and scenario analysis
AI LIMIT
Large-scale Monte Carlo sampling makes repeated risk calculations costly and slow.
QUANTUM–AI ALGORITHMS
FINTELLIQ™ APPROACH
Uses amplitude-estimation methods to reduce the sampling burden in probability and scenario calculations.
DELIVERABLERisk measure · exposure distribution · valuation scenario
MODULE 04DETECTION
Anomaly Detection
금융 이상탐지
BanksCard issuersFinancial institutions
FINANCIAL PROBLEM
Fraud · AML · rare events · changing transaction patterns
AI LIMIT
Scarce labels and high-dimensional patterns reduce sensitivity to rare or emerging anomalies.
QUANTUM–AI ALGORITHMS
FINTELLIQ™ APPROACH
Uses quantum kernels to distinguish complex transaction patterns and detect rare abnormal signals more precisely.
DELIVERABLEAnomaly alert · risk pattern · investigation priority
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.
Quantum Machine Learning
LEARNING ALGORITHM
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
Explainable Quantum AI
QIC EXPLAINABILITY METHODOLOGY
CORE PRINCIPLE
Links Quantum–AI outputs to influential variables, stability diagnostics and reviewable decision evidence.
LIMIT OF AI
Higher predictive performance does not automatically provide the explanations required for regulated decisions.
FINANCIAL ROLE
Credit review · model comparison · governance and validation
MODULE 01 · CROSS-MODULE VALIDATION
Quadratic Unconstrained Binary Optimization
PROBLEM FORMULATION
CORE PRINCIPLE
Converts objectives, choices and constraints into a binary mathematical form that quantum optimizers can process.
LIMIT OF AI
AI predicts outcomes, but does not by itself search the full combination of actions under multiple constraints.
FINANCIAL ROLE
Portfolio allocation · lending rules · rates and credit limits
MODULE 02 · NICE PoC TRACK 02
Quantum Approximate Optimization Algorithm
OPTIMIZATION ALGORITHM
CORE PRINCIPLE
Uses a hybrid quantum–classical loop to search for high-quality solutions to combinatorial optimization problems.
LIMIT OF AI
The number of possible combinations grows rapidly as assets, rules, objectives and constraints interact.
FINANCIAL ROLE
Asset allocation · rebalancing · constrained decision search
MODULE 02
Quantum Amplitude Estimation
ESTIMATION ALGORITHM · MLQAE VARIANT
CORE PRINCIPLE
Estimates event probabilities through quantum amplitudes; MLQAE uses likelihood-based estimation suited to hybrid research.
LIMIT OF AI
Monte Carlo methods require very large numbers of samples for repeated pricing and risk calculations.
FINANCIAL ROLE
CVA/XVA · exposure · derivatives pricing · scenario risk
MODULE 03
Quantum Support Vector Machine
QUANTUM KERNEL CLASSIFIER
CORE PRINCIPLE
Uses a quantum kernel to measure similarity in a high-dimensional feature space and separate complex patterns.
LIMIT OF AI
Rare labels and evolving transaction patterns make emerging anomalies difficult to classify accurately.
FINANCIAL ROLE
Fraud detection · AML signals · rare-event classification
MODULE 04
04 / APPLICATION PROCESS
From financial problem definition to validated decision application.
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 / Credit Intelligence Deep Dive
Connect financial data to credit decisions.
Credit intelligence combines data preparation, hybrid modelling and decision outputs. Each layer has a defined role in the analysis.
01 / Data foundation
Financial data
Income, debt, repayment, cash-flow and behavioural variables.
Alternative evidence
Additional signals selected within applicable governance and data constraints.
Problem framing
Target definition, sampling, imbalance and validation-period design.
02 / Hybrid intelligence
Established model
Operationally understood baseline and interpretable benchmark.
AI baseline
Nonlinear pattern learning and feature interaction modelling.
Quantum–AI model
Selected quantum representation or algorithm applied where the problem structure supports evaluation.
03 / Validation
Predictive performance
Discrimination and error characteristics against baselines.
Calibration & stability
Probability quality, time stability and robustness across segments.
Operational applicability
Explainability, decision rules, workflow fit and implementation constraints.
06 / Industry PoC Example
Evaluate performance under comparable conditions.
01 / DEFINE
Credit question
Define target population, outcome, decision context and success criteria.
02 / PREPARE
Data cohorts
Construct training, validation and time-based comparison cohorts.
03 / MODEL
Parallel baselines
Develop established, AI and Quantum–AI approaches under comparable conditions.
04 / TEST
Multiple criteria
Assess performance, calibration, stability, robustness and explainability.
05 / DECIDE
Operational value
Determine where the approach adds practical value and what is required to deploy it.
NICE Information Service PoC: The collaboration provides an industry context for testing the credit-intelligence methodology against real requirements. Quantitative performance claims will be published only after the relevant evidence and conditions are confirmed.
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.
PARTNERNICE Information Service
INDUSTRYCredit information & lending
PROJECTTwo connected PoC tracks
DATAPseudonymized / de-identified test data
QUANTUM INFRAPasqal neutral-atom system
Expand the evidence available for credit assessment.
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.
01Alternative & financial data
02Classical baseline + QML model
03Credit risk / growth prediction
04XQAI explanation & 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.
01AI credit-risk prediction
02Objectives & operating constraints
03Quantum optimization search
04Rules · rates · limits candidates
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 →