The Cornerstone of Scoring Machine: AI for Predictive Analytics

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At Scoring Machine we are committed to developing technologies that create impactful and smooth experiences and serve as a key source of insights. We did not reinvent the wheel. We built Scoring Machine on top of the strongest AI algorithms, adding some ‘secret sauce’ to make it more powerful.
Scoring Machine is an ML-as-a-service platform that utilizes machine learning (ML) and makes AI meet the current business needs of our customers.

The platform enables financial institutions to seamlessly build, validate and deploy risk models at speed and scale. It is designed to automatically cover the end-to-end ML workflow: from data management to real-time predictions for scoring models. But at the same time we give opportunities for clients to correct results manually too for more control completed result.

Why CROs need Scoring Machine

As technology advances, risk management and data-driven decisions become both more accurate and more complex. Preparing data, checking for correctness, identifying outliers and empty records, and finally, training, building and validating models take too much time, money and manpower.
Scoring Machine is designed to address these gaps through the end-to-end workflow.

● For risk officers: Scoring Machine simplifies the data science behind its system, making it easy to build high-quality models without a data scientist.● For data analysts: Scoring Machine saves hundreds of hours of manual work and makes Big Data easy to understand.
The platform is a fully automatic and autonomous platform, requires no specialized knowledge in mathematical statistics or machine learning.

Scoring Machine: Architecture

The software is a web-based application. The solution uses a custom implementation of the decision tree ensemble method strengthened with a “secret sauce”.
Scoring Machine is based on “supervised learning” algorithms, thus it uses historical data to build risk models specific for each customer.

The platform can work with any kind of data available to a business owner, both structured and “raw”. For example:
● Socio-demographic data● Information from credit bureaus● Transactional data● Data from social media networks and other partners● Other data

Scoring Machine: Use Cases

You can use the platform for a very diverse set of applications. The platform can manage dozens of models across the company for a number of prediction use cases:● Credit/Application scoring● Behavior scoring● Churn rate analysis● Marketing: X-Sell & Upsell● Collection scoring● In-depth data analysis
The platform can analyze large amounts of data both financial and non-financial – with more granularity and deeper analysis – to isolate patterns important for a specific use case.

Scoring Machine: Machine Learning Workflow

The system provides a scalable, and automated workflow to address a five-step process:● Data management● Model training and building● Model validation● Check result -> correct/repeat building model with new settings● In result client can deploy and use completed scoring models with ready rules in him system
Scoring Machine works with historical data, without preliminary analysis and a lot data pre-processing. You just need to prepare the .xls/.xlsx file format that contains the columns (attributes, parameters) and rows (records). Each record must have a status (outcome)*: like GOOD or BAD.
Please note! Personal information (a full name, personal or file numbers, etc.) is not required. If any such data exists, the system automatically identifies and ignores it.
After you build scoring model and test it you can validate the model with detailed report with all valuable insights and data, which every time the system builds with a model, like:● the calculated Gini Index for the test records● recommendations for the best cut-off value● a list of the most important attributes for the model● data that reflect the results of the model performance● and more-more and more other useful information
In result you can easy deploy completed model on your system with ready rules, which you get a very quickly.

Key Benefits

● Fast, fully autonomous, and automated model building process. With a prepared dataset, it takes only 2-10 minutes to build and validate a risk management model. It saves hundreds of hours of manual work for risk officers and data analysts.● High performance and predictive power. Typically, up to +15 Gini Index points compared to traditional models based on logistic regression.● Easy of use – no special training is required to build a model.● Built-in model evaluation and validation tools.● Ability to use raw and big data.
If you need really simple way to build a scoring model -
Sign Up on Scoring Machine and try.