AI Analytics & Prediction: ML Scoring, Anomaly Detection, and Forecasting for Business

Which of these situations sounds like you?

Accuracy and subjectivity

Credit scoring relies on an application form and an officer's experience. A decision takes up to 30 minutes, and its accuracy depends on the human factor.

Predictive maintenance

Unplanned equipment downtime disrupts business operations and raises repair costs 3–5 times compared to planned maintenance.

Customer retention

You find out about customer churn after the fact, and you want to spot risks early and take action to retain customers.

Manual analytics

Processing reports takes up a significant share of work time, and identifying the causes of deviations and priority metrics has to be done manually.

Anomaly detection

Suspicious transactions or anomalous data are detected late, after they've already affected the business, once the chance to respond in time has passed.

Untapped data

Data accumulated over years in BAF, CRM, or Spiro remains unused, even though it could be valuable for decision-making.

AI Analytics & Prediction is an ML model that forecasts events based on accumulated data

The model trains on the company's historical data and generates a forecast: the likelihood a specific customer will churn, expected demand for the next period, the risk associated with a specific transaction, and so on. The result is the ability to act ahead of time instead of analyzing consequences after the fact.

Forecast accuracy depends on the amount, quality, and structure of the available data. That's why every project starts with an AI Feasibility Sprint, which checks whether there's enough data for the forecast, whether the hypothesis is correctly formulated, and what level of accuracy is achievable.

What does the AI Analytics & Prediction implementation process look like?

AI Analytics & Prediction is one of the most complex AI products in terms of the range of effort and resources involved. Pricing is based primarily on an analysis of your data.

Phase 1

AI Feasibility Sprint

from $2,000, 2 weeks

Validating the hypothesis on your data

What we assess: the amount, quality, and structure of the data, the presence of labeled examples (if needed), and the technical feasibility of reaching the expected accuracy.

Result: Go — confirmation the project is worth pursuing, with an accuracy estimate on the available data, a preliminary price, and a timeline. No-Go — a conclusion that the data isn't ready yet, with recommendations on what needs to change.

The Sprint's cost is credited toward the project if you continue with FreshTech.

Phase 2

ML project

from $6,000, depending on complexity

Development and configuration of an ML model for the client's specific task and data, integration with existing systems, validation on a test set with real metrics. A/B testing, if needed.

Phase 3

Ongoing: monitoring and support

from $800/month

Over time, data gets updated, user behavior changes, and new patterns emerge. Without regular monitoring and retraining, the ML model's accuracy gradually declines.

Support includes: monitoring accuracy metrics, retraining the model, updates when business logic changes, and an effectiveness report.

Choose the ML forecasting
scenario for your task

The ML model trains on historical decision data and assigns a probability score to new cases.

Credit scoring for fintech and MFIs, with an explanation of the decisive factors
Churn prediction for SaaS products based on customer behavior patterns
Real-time fraudulent transaction detection
Lead scoring based on ICP and behavioral signals

The system learns to recognize what's normal in your data and automatically flags deviations from typical values in real time.

Financial anomalies: unusual amounts, patterns, or parameter combinations in transactions
Operational anomalies: deviations in performance, resource consumption, or request volume
Input data quality checks before faulty values enter the system

The ML model trains on temporal patterns in your data and forecasts specific future events or values with a confidence interval.

Equipment maintenance needs based on operating conditions and repair history
Product demand accounting for seasonality, trends, and external factors
Account cash balance with a confidence interval, weeks ahead
Executive analytics dashboard with the causes of deviations

Why FreshTech?

Validation before investment

—

Implementation timeline

Launch time depends on the number of data sources

8–10 weeks

Hiring and development can take several months

Costs

Monthly subscription plus business analyst's time

Setup + a transparent ongoing support model

Specialist retainer plus ongoing model support

Forecasting future events

Visualization of existing data, no forecasting of future events

ML-based forecasting of specific events

Technically possible, but requires building a model from scratch

Integrations

Depends on the platform's available connectors

The model integrates with existing systems

Depends on the specific specialist's skills

What does this look like in practice?

Situation: scoring relied on an application form and an officer's experience, with a decision taking 25–40 minutes. The default rate was 12%, above the industry benchmark.

AI Feasibility Sprint: over 8,000 labeled cases with outcomes proved sufficient for an ML model. Test accuracy: 89%.

Solution: an XGBoost-based model trained on 47 features (client profile, behavioral patterns, credit bureau data), with an explanation of the model's decisions and integration with the CRM interface.

Result: decision time dropped from 25–40 minutes to 30 seconds. The default rate fell from 12% to 8.4% over 6 months.

Situation: 1,200+ sites, 4 years of maintenance logs. Emergency callouts made up 23% of volume, and emergency repair costs were three times higher than planned maintenance.

AI Feasibility Sprint: 4,800+ maintenance log records revealed patterns between operating conditions and breakdowns, sufficient for a model.

Solution: a model analyzing maintenance logs, operating conditions, and repair history to predict each unit's breakdown probability over the next 30 days. Integrated with the Field Service App.

Result: the share of emergency repairs dropped from 23% to 11% over 4 months.

Situation: a SaaS product with 2,800 active subscribers and 18 months of historical data. Monthly churn was 3.2%.

AI Feasibility Sprint: the available data proved sufficient for a model. Test-set accuracy for predicting churn within the next month was 74%.

Solution: a model ranks clients by churn risk weekly and generates a list of the top 100 highest-risk clients. CRM integration with an action checklist for each client.

Result: churn among clients the team reached in time dropped from 3.2% to 2.1% over 3 months.

Frequently Asked Questions

Why is the price determined after an AI Feasibility Sprint?

The cost of AI Analytics & Prediction depends on the client's specific data: its volume, quality, and structure. A credit scoring project built on a solid 5-year MFI dataset and one built on a 6-month startup dataset involve very different amounts of work. That's why every project starts with an AI Feasibility Sprint, where we assess the available data and determine the price and timeline.

What if we don't have enough data?

How is AI Analytics & Prediction different from BI tools?

Does the project need support after launch?

Ready to implement AI forecasting?

Start with an AI Feasibility Sprint to find out whether your data is sufficient to build an ML model.

Serhii Kutyr, CEO

[email protected]

AI-powered forecasting: scoring, anomaly detection, demand analysis

Which of these situations sounds like you?

Accuracy and subjectivity

Predictive maintenance

Customer retention

Manual analytics

Anomaly detection

Untapped data

AI Analytics & Prediction is an ML model that forecasts events based on accumulated data

What does the AI Analytics & Prediction implementation process look like?

AI Feasibility Sprint

ML project

Ongoing: monitoring and support

Choose the ML forecasting
scenario for your task

Scoring

Anomaly Detection

Forecasting Engine

Tell us about your task, and we'll help you implement it

Thanks for contacting us!

Why FreshTech?

What does this look like in practice?

Credit Scoring

Predictive Maintenance

Churn Prediction

Frequently Asked Questions

Ready to implement AI forecasting?

Serhii Kutyr, CEO

Thanks for contacting us!

AI-powered forecasting: scoring, anomaly detection, demand analysis

Which of these situations sounds like you?

Accuracy and subjectivity

Predictive maintenance

Customer retention

Manual analytics

Anomaly detection

Untapped data

AI Analytics & Prediction is an ML model that forecasts events based on accumulated data

What does the AI Analytics & Prediction implementation process look like?

AI Feasibility Sprint

ML project

Ongoing: monitoring and support

Choose the ML forecastingscenario for your task

Scoring

Anomaly Detection

Forecasting Engine

Tell us about your task, and we'll help you implement it

Thanks for contacting us!

Why FreshTech?

What does this look like in practice?

Credit Scoring

Predictive Maintenance

Churn Prediction

Frequently Asked Questions

Ready to implement AI forecasting?

Serhii Kutyr, CEO

Thanks for contacting us!

Choose the ML forecasting
scenario for your task