AI platform for structured credit workflows

Turn raw bank credit data into investable structured assets.

ARTedyS AI ingests loan tapes and credit documents, standardizes the data, detects anomalies, scores risk, applies eligibility rules, and generates SPV-ready structuring outputs and investor analytics.

Watch Demo Kruso Kapital Demo Cherry Bank Page Request Access

sample loans

€6.4M

portfolio balance

eligible loans

Portfolio Intelligence Dashboard

Assets Analysed €42M

Average Yield 8.4%

Eligible Pools 2

Upload

Risk

SPV

Report

Balance €6.4M

Avg Rate 7.1%

Expected Loss 1.2%

Projected IRR 8–10%

Group access

ARTedyS R&D, ARTedyX External SPV, ARTedyX Capital Holding.

ARTedyS R&D

ARTedyX External SPV

ARTedyX Capital Holding

Fitch Ratings context

External rating review perimeter: SPV ISIN bond notes.

Where appropriate, the bond notes issued by the SPV may be prepared for external rating review under Fitch structured-finance criteria. The rating target is the ISIN bond note backed by a defined performing asset pool, not ARTedyS AI and not the operating companies.

The credits to be reviewed are the SPV-issued notes backed by performing pools such as SME loans, consumer receivables, auto loans, leasing receivables, credit-card receivables and unsecured consumer loans.

Collateral focusPerforming, granular and diversified asset pools.

Structural featuresOver-collateralisation, liquidity reserve, coverage tests and priority of payments.

Reporting disciplinePool factors, stratifications, arrears, defaults, recoveries and waterfall reporting.

ApproachOutcome depends on collateral quality, structure, servicing, data integrity and applicable criteria.

A target outcome, including above BBB-, may be pursued only subject to Fitch Ratings' own criteria, assumptions, analysis and final committee decision. No Fitch rating is assigned or guaranteed by this demo.

Workflow

One platform. One execution chain.

Upload portfolio tape

Import CSV loan tapes, servicer files, and related credit documentation.

Normalize credit data

Map heterogeneous fields into one standardized structure across jurisdictions.

Run AI analysis

Detect anomalies, build portfolio segmentation, and generate first analytics.

Score risk and eligibility

Estimate expected loss, apply stress scenarios, and select eligible receivables.

Structure SPV output

Generate pool size, tranche sizing, coupon assumptions, and report templates.

Capabilities

Built for credit operations, analytics, and structuring.

Data Ingestion

CSV and spreadsheet upload, field mapping, data validation, and storage orchestration.

AI Portfolio Analysis

Anomaly detection, portfolio clustering, weighted metrics, and quality scoring.

Risk Engine

Expected loss estimation, scenario analysis, and eligibility filtering for pool construction.

Structuring Engine

Pool sizing, senior / equity split assumptions, projected coupon, and report generation.

Investor Output

Investment summary, composition analytics, geography split, and note parameters.

Secure Infrastructure

Auth, database, storage, audit logs, and API functions with optional Supabase backend.

Architecture

Demo architecture ready for Supabase-backed deployment.

Bank Loan Tapes
CSV · XLSX · Documents

→

Ingestion Layer
Upload · Validation · Mapping

→

AI Engine
Normalization · Anomaly Detection

Risk Engine
Expected Loss · Eligibility

→

Structuring Engine
Pool Builder · Tranche Logic

→

Investor Output
Dashboard · Report · Note Inputs