HighRadius

AI features

As the vendor or firm describes them. Notes in small print record what the sources leave open.

1. Cash application agents

   Extract remittance data from emails and portals and auto-match payments to open invoices using remittance, bank and ERP data; all user actions and system recommendations are logged.

   Kind

   Agents and automated actions, Established machine learning and rules

   Suggests or acts

   Acts

   Who controls it

   Vendor

   Availability

   Generally available

   1 sourceSourceAI-Powered Order to Cash (O2C) Software

2. Collections agents

   Analyze payment behavior to predict overdue accounts and delinquent invoices up to 30 days ahead, and send follow-up emails automatically.

   Kind

   Agents and automated actions, Established machine learning and rules

   Suggests or acts

   Suggests and acts

   Who controls it

   Not stated

   Availability

   Generally available

   Control: Role not stated: escalation rules are based on aging and risk category, and the page does not say who sets them.

   1 sourceSourceAI-Powered Order to Cash (O2C) Software

3. Credit agents and AI credit scoring

   Score credit risk in real time from 50+ data channels and set and adjust credit limits based on risk profiles.

   Kind

   Agents and automated actions, Established machine learning and rules

   Suggests or acts

   Acts

   Who controls it

   Vendor

   Availability

   Generally available

   Control: Whether a person approves limit changes is not stated.

   1 sourceSourceAI-Powered Order to Cash (O2C) Software

4. Deductions coding

   Machine learning assigns deduction reason codes and routes disputes for resolution.

   Kind

   Established machine learning and rules

   Suggests or acts

   Acts

   Who controls it

   Vendor

   Availability

   Generally available

   1 sourceSourceAI-Powered Order to Cash (O2C) Software

Unclear or conflicting statements

Where the sources are unclear or disagree, both statements are shown with their links.

Questions to ask

For this offering

• Which agents can change credit limits, release blocked orders or apply cash without a person approving, and where is that configured?
• What do the Mutually Agreed Success Criteria behind the guaranteed KPI improvements contain, how is the baseline measured, and what happens if the KPIs are not met?
• How are auto-matches and reason codes reviewed and reversed, and what does the log record?
• Which models and data providers feed AI credit scoring, and how are scoring changes communicated?
• Does outcome-based pricing apply to these modules, how is the gain share calculated, and how does it relate to the subscription described on the pricing page?

From the diligence question set

1. DQ-06 Does each feature suggest or act? Can it create, post, approve or change records, and where is that limit enforced?
2. DQ-07 Who can change each feature's configuration, prompts, thresholds, data connections or model: your company, our administrators or individual users? Can we restrict and log those changes?
3. DQ-09 Is each output generative or deterministic, what does any confidence score mean, and can a past output be reproduced and explained?
4. DQ-11 How can we test that a feature considered the complete population, and how does it report items it skipped, could not process or excluded?
5. DQ-13 What does the system record for each AI output or action, and can we export and keep those records for as long as we need them?
6. DQ-20 What happens when use exceeds the included amount: overage charges, throttling, or a feature that stops partway through a critical process?

Build questions for this vendor

AI features

As the vendor or firm describes them. Notes in small print record what the sources leave open.

1. AI cash flow forecasting

   Forecasts daily to yearly with models tailored to the business; HighRadius states 95% accuracy up to 12 months ahead.

   Kind

   Established machine learning and rules, Built into the platform

   Suggests or acts

   Suggests

   Who controls it

   Vendor

   Availability

   Generally available

   1 sourceSourceAutomated Treasury Management Software For Accurate Cash Forecasting

2. Payment fraud detection

   Secures every transaction with AI-driven fraud detection, alongside multi-level approval controls.

   Kind

   Established machine learning and rules

   Suggests or acts

   Suggests

   Who controls it

   Vendor

   Availability

   Generally available

   1 sourceSourceAutomated Treasury Management Software For Accurate Cash Forecasting

3. Rule-based cash reconciliation

   Automates daily reconciliation of planned cash transactions against bank transactions with user-defined rules.

   Kind

   Established machine learning and rules

   Suggests or acts

   Acts

   Who controls it

   Customer finance team

   Availability

   Generally available

   1 sourceSourceAutomated Treasury Management Software For Accurate Cash Forecasting

Unclear or conflicting statements

Where the sources are unclear or disagree, both statements are shown with their links.

Questions to ask

For this offering

• How is the 95% forecast accuracy defined and measured, at which horizon, and can we see it on our own history before relying on it?
• What data history does the forecasting model need, and how does it handle changes in our business?
• Can treasury staff see and override the drivers behind each forecast?
• Which payment actions, if any, can the AI take, and how do approval controls apply to them?
• Does outcome-based pricing apply to the treasury modules, and how would forecast accuracy be measured for a gain share?

From the diligence question set

1. DQ-09 Is each output generative or deterministic, what does any confidence score mean, and can a past output be reproduced and explained?
2. DQ-11 How can we test that a feature considered the complete population, and how does it report items it skipped, could not process or excluded?
3. DQ-22 What has to be in place before each feature works as described: data history, configuration, integrations, edition and services?
4. DQ-23 How will you support our validation before we rely on a feature: a test environment, test data, parallel runs and documented known limitations?
5. DQ-07 Who can change each feature's configuration, prompts, thresholds, data connections or model: your company, our administrators or individual users? Can we restrict and log those changes?

Build questions for this vendor