Validis (US)

By Validis Group Holdings Limited

Overview

Validis connects ChatGPT to transaction level accounting data extracted from a company's source ledgers. Search and select engagements, pull standardized general ledger, accounts receivable, accounts payable, and trial balance data through a single consistent schema, regardless of the underlying accounting system. Run built-in analytics including financial ratios, AR and AP aging, working capital, revenue quality, ledger integrity, and fraud indicators, or query the underlying data directly. Designed for lenders, auditors, and finance teams who need fast, reliable access to financial data inside their AI workflows.

Tools

analytics_ap_aging

ChatGPT

Accounts payable aging buckets as-of a date.

analytics_ar_aging

ChatGPT

Accounts receivable aging buckets as-of a date.

analytics_balance_sheet_quality

ChatGPT

Run balance sheet quality analyses on cached GL data and derived financial statements. Analyses: ebitda_working_capital_bridge (bridge EBITDA to working capital changes and operating cash flow per month), accruals_buildup (detect accounts with sustained accrual balance growth over consecutive months), prepayment_accrual_trend (track prepayment, accrual, and deferred account balances over time), balance_sheet_rollforward (opening + activity = closing per BS category per month), unusual_liability_fluctuations (flag months with liability changes exceeding N standard deviations), suspense_clearing_accounts (activity and uncleared balances in suspense, clearing, and intercompany accounts). Supports optional materiality filtering to exclude immaterial items from results.

analytics_financial_ratios

ChatGPT

Compute financial ratios and metrics organised by category: liquidity (current ratio, quick ratio, cash ratio, working capital), profitability (margins, ROA, ROE, ROCE), leverage (debt-to-equity, interest coverage), efficiency (DSO, DPO, DIO, cash conversion cycle, turnover), cash flow (operating CF ratio, free cash flow), growth (MoM changes), and aging (AR/AP concentration and weighted average days). Returns a monthly time-series for most ratios plus point-in-time aging ratios. Each ratio includes its value, formula components (numerator/denominator), and interpretation guidance.

analytics_financial_statements

ChatGPT

Run financial statement analyses derived from P&L and balance sheet working papers. Analyses: trend_analysis (monthly/quarterly trends with moving averages), variance_analysis (period-over-period variance with significance flags), common_size_statements (all items as percentage of revenue or total assets), vertical_horizontal_analysis (combined vertical percentage and horizontal change from base period), account_balance_volatility (coefficient of variation per BS category), expense_ratio_analysis (expense categories as percentage of revenue over time), revenue_expense_correlation (Pearson correlation between monthly revenue and expenses). Supports optional materiality filtering (though all analyses in this tool produce aggregate ratios, so no item-level filtering is applied).

analytics_fraud_indicators

ChatGPT

Run fraud indicator analyses on cached GL/AR/AP data. Analyses: reversal_reclassification (opposite-amount pairs within time window on same account), unusual_account_combinations (rarely-used accounts with significant amounts), topside_adjustments (large manual journal entries near period end), vendor_payment_patterns (AP vendor payment regularity and anomalies), customer_refund_analysis (credit note and refund patterns per customer), related_party_indicators (name matching between AR customers and AP vendors via token similarity). Each analysis can be filtered by date range and account codes. Supports optional materiality filtering to exclude immaterial items from results.

analytics_gl_summary

ChatGPT

General ledger summary grouped by month. Optionally sum absolute amounts (transaction volume) instead of signed net amounts.

analytics_ledger_integrity

ChatGPT

Run ledger integrity analyses on cached GL/AR/AP data. Analyses: benfords_law (leading-digit distribution vs Benford's expected, with chi-squared test; supports segment_by to produce per-segment distributions by journal_type, coa_primary_category, or coa_sub_category), duplicate_detection (matching amount + counterparty within time window), gap_sequence_testing (gaps in document/journal number sequences), journal_entry_analysis (manual postings, unusual users, late adjustments), round_number_frequency (amounts divisible by round thresholds), outlier_detection (statistical outliers via IQR or z-score), after_hours_posting (weekend and off-hours posting patterns). Each analysis can operate on the full dataset or be filtered by date range, account codes, journal_type, and COA categories. Supports optional materiality filtering — applied as post-filter for item-level analyses or as SQL pre-filter for aggregate analyses (Benford, round numbers, after-hours).

analytics_query

ChatGPT

Ad-hoc analytics using a safe JSON query DSL compiled to parameterized SQL.

analytics_revenue_quality

ChatGPT

Run revenue and earnings quality analyses on cached GL/AR data. Analyses: revenue_cutoff_testing (revenue entries near period boundaries), period_end_revenue_spikes (disproportionate revenue in last days of month), revenue_concentration (top customers by revenue with Pareto cumulative %), credit_note_reversal_trends (monthly credit note trends and ratio to gross), gross_margin_trend (gross margin percentage over time), non_recurring_journal_id (non-recurring entries by journal type, keywords, and amount percentile), ebitda_normalization_candidates (items potentially needing EBITDA normalization above materiality threshold), manual_revenue_journals (manual entries to revenue accounts), deferred_revenue_movement (deferred revenue balance changes by month). Supports optional materiality filtering to exclude immaterial items from results.

analytics_run_full_suite

ChatGPT

Run a predefined analytics suite (data quality, GL monthly summary, AR/AP aging) over cached datasets.

analytics_working_capital

ChatGPT

Run working capital analyses on cached AR/AP data and derived balance sheet. Analyses: working_capital_trend (net working capital components over time: cash, AR, inventory, AP, overdraft), customer_payment_behavior (days to pay per customer from settlement timestamps), vendor_payment_timing (days to pay per vendor from settlement timestamps), overdue_balance_concentration (top customers/vendors by overdue balance), large_customer_exposure (top customers by outstanding AR balance with concentration %), large_vendor_dependency (top vendors by outstanding AP balance with concentration %). Note: AR aging, AP aging, DSO, and DPO are available via analytics_ar_aging, analytics_ap_aging, and analytics_financial_ratios. Supports optional materiality filtering to exclude immaterial items from results.

datasets_refresh

ChatGPT

Fetch/cache General Ledger, Accounts Receivable, Accounts Payable, and Trial Balance datasets for an engagement_id. The refresh process can take up to 10 minutes to complete.

datasets_sample_rows

ChatGPT

Return a small, bounded sample of cached canonical rows with audit-oriented filters (monetary thresholds, fraud indicators, random sampling, overdue items, and more).