of borrowers linked to their group and related parties at application
CREDIT/Faster decisions on a sounder book
Assess borrowers faster, decide more consistently, and spot deterioration earlier.
Lameh shows credit teams each borrower with its group and its filings, and states the institution’s own credit experience as rules.
Request demo
rules proposed from past credit files, each linked to its cases
faster from application to credit decision
From application to annual review, each decision informed by the ones before it.
The borrower from its own filings
Proprietary Data holds one record per company with its registration, legal form, and subsidiaries, financial statements for listed and unlisted companies on one taxonomy, and owners and related parties linked entity to entity. The assessment starts from figures linked to their filings.
Proprietary Data
Credit rules proposed from past files
Knowledge Induction extracts terms, figures, and dates from past credit files and agreements and compares them across cases. The terms the institution usually accepts and the conditions that preceded past losses are proposed as rules.
Knowledge Induction
Exposure to the whole group
With Ontology, a borrower and its related entities are linked to one another and to every facility recorded against them. Exposure is computed the same way at approval and in monitoring, against limits held in the same place.
Ontology
Explore the suite of products
Proprietary Data
Verified, granular data on the region’s companies, markets, and economy, with each figure linked to the source that published it.
Knowledge Induction
Patterns in the institution’s past work, proposed as rules and linked to the cases that support them.
Ontology
One shared definition of the organizations, instruments, positions, and transactions the institution works with.
Institutional Record
Decisions, knowledge, and working methods recorded as the work happens and retained by the institution.
FAQ
Does the data cover unlisted borrowers?
Yes. Company reference holds one record per registered entity, and unlisted companies have income statements, balance sheets, and cash flows mapped to the same taxonomy as listed issuers.
How is a borrower’s group identified?
Each company record carries its subsidiaries, and substantial shareholders, partners’ shares, and related-party transactions are linked entity to entity. The ontology keeps who owns what and who is exposed to whom.
How does past loss experience inform new decisions?
Facts from past files are compared across cases to show the conditions that preceded losses. What recurs is proposed as a rule, and once approved it is applied by people and AI models.
How does Lameh verify the figures it provides?
Each figure is read from the official source that published it, kept as reported, and linked to the document it came from. Where a figure appears in more than one source, the values are reconciled.
How does Lameh work with the systems we already use?
Lameh links the records held in separate systems to the organizations, instruments, positions, and transactions they describe. Each is defined once and mapped to its records in every source system.
How are changes proposed by an AI model controlled?
Changes are made through defined actions, each with its conditions, approvers, and log. A limit increase follows the same approval path whether a person or an AI model proposes it.
Does the institution depend on a single AI model?
No. Each prompt is routed to the model best suited to it, weighing reasoning, accuracy, speed, and cost, and new models enter the routing as they are released.
Credit judgment the institution keeps
Each borrower from its own filings, exposure across the whole group, and your institution’s credit experience stated as rules.
Request demo