Simplify ML deployment with Decisimo: A clear platform for decision making - Decisimo
Decisimo as a tool for deploying ML models
Decisimo is a practical platform for deploying machine learning models trained in Python or R.
It lets you integrate these models into a broader decision-making process, which makes it useful when businesses and organizations rely on machine learning for decisions.
Building Decision Flows with Models
One of the main capabilities of Decisimo is building a decision flow. That makes it straightforward to place a model trained in Python or R inside a broader decision-making process, such as probability assessment or segmentation.
Decisimo also supports optimization models, which can help determine the best offer or combination and make the decision-making process easier to manage.
Chaining Models and Incorporating Decision Components
Another useful capability of Decisimo is chaining models together into decision logic.
This means multiple models can run within a single decision-making process, which can improve consistency and reliability.
Decisimo also supports other decision components, such as rule sets, decision tables, and mathematical functions, which adds more control to the decision-making process.
Examples from various industries and applications
Decisimo's ability to build decision flows and incorporate pre-trained models makes it a flexible platform for a wide range of industries and applications.
Credit Scoring Model in Consumer Lending
For example, in consumer lending, a machine learning model trained in Python or R could be used to predict the creditworthiness of an applicant based on their financial history and other factors.
That output could then be incorporated into a decision flow in Decisimo, where it would be used to determine the appropriate interest rate or loan terms for the applicant.
Risk-based Pricing and Product Proposal
The model output could also be used to propose a different product or a lower or higher down payment.
This risk-based pricing approach can help lenders make better lending decisions while also supporting fair and transparent lending practices for borrowers.
Marketing and Customer Service
Decisimo can also be used in marketing, customer service, and many other fields. For example, in customer service, a machine learning model trained in Python or R could be used to predict the likelihood of a customer requesting a refund.
That probability output could then be incorporated into a decision flow in Decisimo, where it would be used to determine the best course of action, such as providing a discount or offering an alternative product.
Advantages of Decisimo over traditional ML operations
Decisimo offers a clear advantage over traditional machine learning operations, where a separate model platform must be built and then integrated with other decision support systems.
With Decisimo, machine learning models and decision logic sit together in one platform, and teams have a single place to manage decisions.
This removes the need for complex, time-consuming integration work and supports a simpler, more efficient approach to decision-making.
User-friendly Interface and Management of the Decision-making process
Decisimo also provides a clear interface for managing the decision-making process.
This makes it easier for teams to collaborate on decision-making projects and supports more transparency and accountability.
No-code Platform and Business User Empowerment
One of the main benefits of Decisimo is that it does not require coding skills or knowledge of complex topics such as programming languages or cloud operations.
It is a no-code platform that lets business users update decision logic, change rules, and deploy quickly to production.
This means non-technical users can make changes to decision-making processes and keep them aligned with changing business needs.
Decisimo as a complete decision-making solution
Overall, Decisimo is a practical platform that simplifies deploying machine learning models and placing them inside a broader decision-making process.
It provides a complete decision-making solution that removes the need for complex integration work and supports a simpler, more efficient approach to decision-making.
Frequently asked questions
How should a lender choose a cutoff for a credit model?
Set the cutoff against the lender’s approved risk appetite and the cost of false approvals and false declines, then have the credit team review and approve the resulting decision logic. Compare outcomes across applicant segments and check how many applications fall near the cutoff. A score alone does not determine the right threshold, and a cutoff that works for one product or applicant mix may not work for another.
What should a lender test before putting a model into a credit decision flow?
Replay historical applications through the model and the proposed decision logic, then compare approvals, declines, pricing and outcomes with the current approach. Check that the decision flow handles missing or unexpected model outputs as intended, and review results across applicant segments. Have the credit team approve the changes before release. Historical testing helps find problems, but it cannot guarantee future performance.
When should a lender put models and decision logic on the same platform?
Consider a combined platform when the team needs to manage model outputs alongside rule sets, decision tables or mathematical functions, and wants policy changes reviewed in the same place. Compare that option with keeping model serving separate, including integration effort, ownership of changes and how each system records revisions. A combined platform is not automatically simpler if existing integrations already meet the team’s needs.
When should a lender send an application for human review instead of deciding from a model score?
Set a review range around the score cutoff when uncertainty or the cost of a mistaken decision justifies a person’s assessment. The credit team should define and approve the range, the information reviewers can use and the action they may take. Review capacity matters: a range that sends too many applications for manual assessment can slow decisions and overwhelm the team.
