INTELLIGENT ACCOUNTING AND AUDITING FOR START-UPS: AN AI-BASED FRAMEWORK FOR ERROR DETECTION, FRAUD PREVENTION, AND FINANCIAL SUSTAINABILITY

Abstract

Start-ups frequently experience accounting errors, weak internal controls, cash-flow instability, and heightened exposure to occupational fraud because of limited financial expertise and resource constraints. This study develops an artificial intelligence–based accounting and auditing framework for automated error detection, fraud prevention, and financial-sustainability assessment in start-up enterprises. The analysis employed an anonymized three-year dataset containing 240,000 financial transactions from 120 technology, retail, service, manufacturing, and e-commerce start-ups. The dataset included 48 financial, transactional, behavioral, temporal, and organizational variables, while 5.2% of records were independently verified as erroneous or fraudulent. Data preparation involved duplicate removal, missing-value imputation, categorical encoding, robust normalization, feature selection, synthetic minority oversampling, and leakage-free chronological partitioning into 70% training, 15% validation, and 15% testing subsets. Logistic regression, support vector machine, random forest, XGBoost, LightGBM, and an attention-enhanced deep neural network were comparatively evaluated using accuracy, precision, recall, F1-score, area under the receiver operating characteristic curve, and false-negative rate. The proposed model achieved 96.7% accuracy, 94.8% precision, 93.9% recall, a 94.3% F1-score, and an AUC of 0.984, outperforming the strongest conventional benchmark, XGBoost, by 2.6 percentage points in accuracy. It reduced undetected fraud by 38.4% and accounting-classification errors by 41.7% compared with rule-based auditing. Explainable AI analysis identified unusual payment timing, invoice duplication, abnormal expense ratios, rapid beneficiary changes, transaction splitting, and inconsistent authorization patterns as the most influential risk indicators. The sustainability module combined predicted cash flows, liquidity ratios, revenue volatility, burn rate, and working-capital adequacy to classify financial distress with 92.6% accuracy and provided warnings an average of 11.4 weeks before conventional ratio-based assessment. Simulated implementation reduced audit-review time by 46.2%, manual testing workload by 39.8%, and estimated annual compliance costs by 24.7%. The findings demonstrate that integrated AI can strengthen continuous assurance, improve fraud responsiveness, and support proactive financial planning in resource-constrained start-ups. It supports transparent governance and evidence-based allocation of scarce resources. The framework offers founders, auditors, investors, and regulators a scalable decision-support mechanism, although external validation across jurisdictions and longer operating histories remains necessary.