# Sample Projects

Contents

At the end of each sample, there is a link to download the complete project. Each project has a folder named sampleJSON which contains sample inputs.

### Flight Management

This tutorial will use the scenario of checking the eligibility of a pilot as a means of scheduling flight duties. There are rules and regulations defined by the aviation authority, such as the maximum flight period, the maximum number of night shifts that a pilot can work, etc. For example, the maximum duty period should be 12.5 hours. The process contains a set of important steps.

1. Calculate the daily flight duty period
2. Count the number of landings
3. Calculate flight time
4. Check the eligibility of pilot on duty

Used Technologies: Decision Graph, Decision Table, Flow, Fact Concept, Business Formulas

Industry: Aviation

### Delivery Cost Calculator

Create a simple Decision Table that calculates delivery costs based on the total weight of a parcel. In a post office, this is usually based on a couple of conditions and business rules, but for the sake of this tutorial, let’s make it simple, as shown in the business rules below:

• If the Package is between 0 and 22 kg then the delivery cost is 30\$
• If the Package is between 22 and 50 kg then the delivery cost is 33\$
• If the Package is between 50 and 110 kg then the delivery cost is 120\$
• If the Package is heavier than 110 kg then the delivery cost is 200\$

Remark: The tutorial shows how you can run a decision table in the FlexRule Server and test it in Postman.

Used Technologies: Decision Table, Business Rules

Industry: Manufacturing

Defining rules to validate the names and addresses of an address book. Natural Language is used to model business rules related to data validation and navigate data relations.

Remark: This example shows how to use a core library in natural language.

Used Technologies: NL or Natural Language, Fact Concept

### Car Insurance

This is a sample for the car insurance industry that calculates the premium of a car based on Auto and Base premium components. The business rules are for risk classification and calculation of the premium of a car. This example requires an SQL database connection.

Remark: This example shows how to reference a .Net assembly (.dll) that defines types to be used as part of rules modeling and execution.

Used Technologies: Decision Graph, Decision Table, Flow, Information Requirement Diagram or IRD, Input Validation Logic, Natural Language, Fact Concept, Business Formulas

Industry: Insurance

### Student Pass-Fail State

Using Natural Language, validate the input and finally decide the pass-fail state of a student. In the end, it will create the JavaScript code for the Natural Language code and can be tested in the JavaScript Testbed.

Remark: The tutorial shows how to test a sample project in JavaScript Testbed.

Used Technologies: NL or Natural Language, Fact Concept, Flow, Business Glossary.

Industry: Education

The process of determining the eligibility of providing firearm licenses contain a set of serious rules that require 100% accuracy as it is related to security. The example will show how FlexRule can be used to automate this process.

1. Read data from the previous applicant list
2. Check whether the applicant is already applied
3. For previous applicants, get the previous score
4. For new applicants, calculate the score
5. Determine eligibility

Remark: This tutorial shows how to read data from a CSV file.

Used Technologies: Decision Table, Fact Concept, General Flow

Industry: Security

### Liability Fee Calculation

Calculate a premium for a given liability amount based on these rules:

• Up to and including \$50 000 –> fixed amount of \$500.00 (Base premium)
• \$50 001 to \$100 000 –> Add \$40.00 for each \$10 000 increment
• \$100 001 to \$200 000 –> Add \$30.00 for each \$10 000 increment
• \$200 001 to \$300 000 –> Add \$20.00 for each \$10 000 increment
• \$300 001 to \$1 000 000 –> Add \$10.00 for each \$10 000 increment
• \$1 000 001 to \$3 000 000 –> Add \$7.00 for each \$10 000 increment
• \$3 000 001 to \$5 000 000 –> Add \$10.00 for each \$10 000 increment

Industry: Insurance

### Home Loan Calculator

A sample solution in order to create a home loan borrowing calculator.

The application gets information about the applicant’s personal details, earnings, and expenses as the input and as the output, it shows how much the applicant can borrow. We can also check whether the applicant is an existing customer of the bank by reading the database. This example requires an SQL database connection.

Remark: This example shows how to use a validator to validate input parameters.

Used Technologies: Decision Table, General Flow, Fact Concept

Industry: Financial Services

### COVID-19 Symptom Checker

This is a sample for a symptom checker customized for checking the symptoms of COVID-19. The model takes the symptoms and certain other details of a patient and as the output, it provides recommendations to show what to do next. This model can be easily customized to provide recommendations for any disease.

Used Technologies: Decision Table, Decision Graph, Business Glossary, Fact Concept

Industry: Healthcare

The tutorial will use the scenario of checking the symptoms of a patient and alert the carer with the next best action to be taken.

1. The patient can select the symptoms and their status
2. Then the system will go through a set of business rules to do the initial diagnosis and decide the next best action to be taken
3. Finally, an alert email will be sent to the carers along with the next best action

Remark: This example shows how to use the email activity to send an email.

Used Technologies: Decision Table, Flow, Business Expressions, Decision Graph, Business Glossary, Fact Concept

Industry: Healthcare

### Tiebreaker

The sample project shows how to implement tiebreaking rules of a game to determine the ranks of the players.

• Input –> A lit of players with their details such as ranking points, the number of matches, age, the previous rank
• Decision –> Rank the players. If there are players with the same ranking points use the tiebreaking rules to rank the players.
• Output –> The list of players with their rank

Remark: This example shows how to use a flow and a Decision Table.

Industry: Sports

### Insurance Policy Discount

In this example, we will show how to add additional costs or discounts to a base premium without accessing the project that calculates the base premium. This restricts editing the basic part of the project and ensures that it is kept untouched.

• Get the discount percentage.
• Apply the discount percentage to the base premium to calculate the health premium.

Remark: This example shows how to use a decision package.

Used Technologies: Decision Package, Decision Table, General Flow, Business Expressions

Industry: Insurance

### Flight Reschedule

In this tutorial, we are going to demonstrate the behavior of a Decision Requirement Diagram using an example of a flight rescheduling scenario.

1. Get canceled flights
2. Determine passenger queue
3. Reassign passengers
1. Pick the first passenger in the queue
2. Identify the best Active alternative flight
3. Assign the alternative flight to the passenger
4. Keep reassigning passengers until all the passengers in canceled flights are reassigned to active flights

Remark: This example shows how to use different logic documents and glossaries along with a DRD to define the decision hierarchy.

Used Technologies: Decision Graph, Natural Language, Business Formulas

Industry: Aviation

### First Home Buyer Credit Decision

This example is about checking the eligibility of a first home buyer loan for a given individual. It checks for income, expense, and affordability and assesses the customer as a first home buyer.

1. Read the income and expenditure data of the customer.
2. Check for the income consistency/ frequency, minimum income, and dole payments to make the income decision.
3. Calaculate income expense ratio to make the expence decison.
4. Calculate the efficiency ratio to get the credit score.
5. Determine the final outcome of the credit decision.

Remark: This example shows how to use different logic documents and glossaries along with a DRD to define the decision hierarchy.

Used Technologies: Decision Table, Flow, Business Expressions, Decision Graph, Business Glossary, Fact Concept

Industry: Financial Services

### School Staff and Student Validation

We will be using a scenario of validating the school staff and student relations. This example can be run in JavaScript Environment. We can also test the login in the built-in JS Test Bed.

Remark: This example shows how to use Decision Tables and Natural Language to model business rules to execute in a JavaScript environment.

Used Technologies: Decision Table, Natural Language, JavaScript

Industry: Education

### Bill Calculator

This example is about calculating usage bill based on user consumption data.

1. Usage Breakdown based on Tiers.
2. Calculate the hourly cost for different tiers.
3. Calculate the total payable amount.

Remark: This example shows how to use different logic documents and glossaries along with Decision graphs to calculate usage bill based on different usage tiers.

Used Technologies: Decision Table, Decision Graph, Business Formulas, Fact Concept

Industry: Financial Services

### Standard Rating

This example is about determining standard ranked ratings based on external credit rating data.

1. Standardizing Credit Rating
2. Credit Rating Rank.
3. Determine Standard Ranked Ratings.

Remark: This example shows how to use different logic documents such as Decision Tables and Decision graphs to determine standard ranked ratings based on different external credit rating agencies’ data.

Used Technologies: Decision Table, Decision Graph, Fact Concept

Industry: Financial Services

### Leave Calculator

This example is about calculating employee vacation days based on company policies.

1. Calculate base vacation days
2. Calculate extra vacation days
3. Calculate total vacation dates

Remark: This example shows how to use different logic documents such as Decision Tables and Decision graphs to calculate employee vacation days.

Used Technologies: Decision Table, Decision Graph, Fact Concept

Industry: Financial Services

### Medical Claim Processing

This example is about finding incompatible medical diagnosis codes in medical claims.

1. Read the dataset, build a lookup table using the dataset.
2. Search through the lookup table and create error messages for incompatible diagnosis code.

Remark: This example shows how to utilize cache memory for a efficient data search in a given data set and create error notifications as an output.

Used Technologies: Decision Table, Generic Flow, Business Formulas, Cache Memory Management

Industry: Health Insurance

### Credit Card Approval Process

This example is about creating a custom scorecard for credit card approval decision.

1. Create Standard Scorecard.
2. Automatic Approval.

Remark: This example shows how to use different logic documents such as Decision Graph, Decision Table and Natural Language Document for credit card approval decision.

Used Technologies: Decision Table, Decision Graph, Natural Language Document

Industry: Financial Services

### Driver Risk Assessment

This example is about creating a driver risk assessment and how to refactor a business decision.

1. Determine the risk of a vehicle type
2. Determine the driver risk level

Remark: This example shows how to refactor decision table using Test cases and Decision Graph.

Used Technologies: Decision Table, Decision Graph, Business Terms, Fact Concept, Test Cases

Industry: Financial Services

### Borrowing Ability in a Library

This example is about how to automate the library policies with regards to determining borrowing ability of a student.

1. Determine the loan details based on the loan period.
2. Calculate overdue days.
3. Determine Overdue Status.
4. Calculate Fine.
5. Determine Borrowing Ability.

Remark: This example shows how to use Decision Graph and Decision Tables to automate policies.

Used Technologies: Decision Table, Decision Graph, Fact Concept, Test Cases

Industry: Education Services

### Online Booking Fraud Detection

This example is about an online booking fraud detection system using straight-through processing and using human workflow.

1. Straight through processing for determining Fraud Decision
2. Fraud Analyst’s Decision – Human Workflow

Remark: This example shows how to use Decision Graph, workflow, and Decision Tables to determine online booking fraud based on Fraud Rating Score.

Used Technologies: Decision Table, Decision Graph, Fact Concept, Workflow

Industry: Financial Services

### Automated Credit Card Eligibility

This example is about an automated credit card eligibility model using straight-through processing.

1. Determining demographic suitability
2. Determining credit card eligibility

Remark: This example shows how to use a Decision table, Generic Flow, and Business Formulas to determine credit card eligibility.

Used Technologies: Decision Table, Decision Graph, Fact Concept, Business Formulas, Generic Flow

Industry: Financial Services

### Residency Permit Eligibility

This example is about an implementing residency permit eligibility regulations.

Remark: This example shows how to use Decision Graph, Decision table, Generic Flow, and Business Formulas to implement regulations for determining residency permit eligibility.

Used Technologies: Decision Table, Decision Graph, Fact Concept, Business Formulas, Generic Flow, Test Cases

Industry: Government Services

This example is about implementing Insurance Underwriting Business Rules in three different ways.

Remark: This example shows how to use Decision Table, Natural Language Document, Fact Concept and Business Glossary to implement Insurance Underwriting Business Rules.

Used Technologies: Decision Table, Natural Language Document, Fact Concept, Business Glossary

Industry: Insurance

Remark: This example shows how to use a Decision Graph, Decision Table, Fact Concept, and Business Glossary to implement Insurance Business Rules.

Used Technologies: Decision Graph, Decision Table, Fact Concept, Business Glossary

Industry: Insurance

This example is about implementing automated Insurance premium calculation with eligibility score.

Remark: This example shows how to use a Decision Graph, Decision Table, Fact Concept, and Business Glossary and workflow to implement premium calculation in insurance.

Industry: Insurance

Used Technologies: Decision Graph, Decision Table, Fact Concept, Business Glossary, WorkFlow

### Direct Car Insurance Claim Settlement

This example is about implementing Car Insurance Claim Settlement aligned with RDR Convension.

Remark: This example shows how to use a Decision Graph, Embedded Models, Decision Table, Fact Concept, Business Glossary and workflow to implement insurance claim settlement guidelines.

Industry: Insurance

Used Technologies: Decision Graph, Decision Table, Fact Concept, Business Glossary, WorkFlow

## Data & Analytics

The example is about calculating the health insurance price for a given individual. We have used the AutoML capability of FlexRule as a part of this project.

1. Predict the charges based on age, sex, BMI, number of children, smoking preference, and region
2. Calculate the extra charges
3. Calculate temporary charges
4. Calculate the premium (predicted charges + extra charges + temporary charges)

Remark: This example shows how to calculate the health insurance premium using the analytics extension’s AutoML Builder to make predictions along with decision tables.

Used Technologies: Decision Table, Business Expressions, Decision Graph, Fact Concept, Data Analytics, Predictive Analytics, AutoML

Industry: Insurance

### Patient Condition Detection

This example is about detecting the patient’s conditions. As the output, the doctor will receive an alert text message to the mobile phone. We have also used the Business Rules Mining capability of FlexRule as a part of this project.

1. Get patient test results from various laboratories
2. Detect whether there is any condition
3. Alert the doctor via SMS, if there is any condition

Remark: This example shows how to use the Business Rules Mining capability of FlexRule to mine business rules from a given dataset.

Used Technologies: Decision Table, Business Expressions, Decision Graph, Fact Concept, Data Analytics, Business Rules Mining

Industry: Health

### Predicting Flower Type

The example is about predicting the flower type with a custom-made algorithm using the PMML capability in FlexRule.

We have created a PMML file of a trained model using Python and connect that to make predictions. These predictions can be used as a part of a DRD to make decisions.

Remark: This example shows how to read a PMML model and make predictions using the analytics extension.

Used Technologies: General Flow, PMML, Data Analytics, Predictive Analytics

Industry: Environment

### Detecting Breast Cancer

The example is about detecting breast cancer with a custom-made algorithm using the PMML capability in FlexRule. In the end, the system will be able to determine whether the patient has the possibility of having breast cancer.

We have created a PMML file of a trained model using Python and connected that to make predictions. These predictions can be used as a part of a DRD to make decisions.

Remark: This example shows how to read a PMML model and make predictions using the analytics extension.

Used Technologies: General Flow, PMML, Data Analytics, Predictive Analytics

Industry: Healthcare

### Determine Loan Interest Rate

This is a sample to determine the interest rate of an application using the applicant’s personal and account data. We will also be using an in-built machine-learning algorithm to predict the payment delays and based on that the final interest rate is determined. This example requires an SQL database connection.

1. Input user ID and amount to burrow
2. Retrieve data from the database
3. Predict payment delay
4. Determine repayment risk score
5. Determine Interest Rate

Remark: This example shows how to read data from a CSV file and make predictions using the analytics extension.

Used Technologies: Decision Table, Flow, Business Expressions, Decision Graph, Business Glossary, Fact Concept

Industry: Financial Services

### Analysing Bank Details

The data flow and ETL (Extract, transform, load) process were automated using the IRD (Information Requirement Diagram). The example will get details of a list of bank accounts and determine/calculate the following facts.

• Maximum balance
• Minimum balance
• Average of bank accounts
• Summation of balances
• Find the latest and oldest Bank accounts by Account opened date

Remark: This example shows how to extract, transform, load data.

Used Technologies: General Flow, IRD, or Information Requirement Diagram

Industry: Financial Services

### Collections

This project contains a set of scenarios that demonstrate the capabilities of FlexRule for manipulating data collections.

• Car: Using IRD (Information Requirement Diagram) or NL (Natural Language), it finds a set of unique and common cars from a given list.
• Person: Using DT, it finds the set of unique people according to their name and sex.
• Titanic: Using NL, it finds a set of males and females who survived or did not survive during Titanic. The data is read from an Excel spreadsheet.

Remark: This example shows the capabilities of data manipulation.

Used Technologies: Decision Table, Flow, IRD (Information Requirement Diagram), Natural Language

### Golf Weather Forecast

This example takes a dataset of weather details to train the model and finally, predicts the possibility of playing golf. It uses the C45 algorithm for predictions. It will:

• Input data
• Train
• Predict

Remark: This example shows how to read data from a CSV file and make predictions using the analytics extension.

Used Technologies: General Flow, CSV File Reading, Data Analytics, Predictive Analytics, C45 Algorithm

Industry: Sports

### Medical Diagnosis

This example takes a dataset of symptoms to train the model and finally, diagnose whether the patient has cold, flu, or allergies. It uses the Naive Bayes algorithm for predictions.

• Input data
• Train
• Predict

Remark: This example shows how to read data from a CSV file and make predictions using the analytics extension.

Used Technologies: General Flow, CSV File Reading, Data Analytics, Predictive Analytics, Naive Bayes Algorithm

Industry: Healthcare

## Decision Robotics

### Car Insurance Fraud Detection

This tutorial shows how to detect a car insurance fraud using a given dataset and send an alert email along with the car registration details. When a claim is submitted,

The process contains a set of main steps:

1. Detect Frauds using a dataset.
2. If fraud is detected, check car registration information from the public directory.
3. If there is an error in the registration number, send a notification.
4. Create the email alert message to inform the authority.
5. Send the email alert to the required authorities.

Remark: This example shows how to use analytics and the decision robotics web tools.

Used Technologies: Decision Robotics, AutoML/ Data Analytics, Decision Table, Flow, Business Expressions, Fact Concept

Industry: Insurance

Automate the process of reading emails of a recruitment email account to download applications and candidates’ details.

1. Input the email address, and password of the recruitment email account and the job position that needs to be filtered.
3. Store them in separate folders according to the email address and date

Used Technologies: General Flow, Mail Server, Automate Email Reading

Industry: Recruitment

### Filtering Résumés

Automate the process of reading résumés to check whether the required skills are presented.

1. Load the résumé (PDF file)
2. Search for each keyword
3. Count the matching keywords
4. Calculate the matching keyword percentage

Remark: This example shows how to use the PDF extension to read résumés in PDF format.

Used Technologies: General Flow, Automating PDF Document Reading, Keyword Search

Industry: Recruitment

### Student Record

Automate the process of reading data from an Excel file to get student records, calculate the total, average, and pass/fail state and write back to the file.

1. Open the Excel sheet where you have saved student scores
2. Calculate the Total
3. Calculate the average
4. Depending on the average determine pass/fail state
5. Save the Excel sheet with a defined filename

Remark: This example shows how to use the decision robotics Excel tools.

Used Technologies: General Flow, Decision Robotics for Excel Documents

Industry: Education

## Human Workflow

### Loan Origination

Automate the workflow of the loan origination process

1. Collect the applications
2. Validate applications
3. Check eligibility according to the bureau’s standards
4. Determine loan application routing
5. Team review if required
6. Determine whether the loan is approved

Remark: This example shows how to use a workflow with multiple roles and outcomes.

Used Technologies: Decision Table, Workflow, Business Expressions, Decision Graph, Business Glossary, Fact Concept

Industry: Financial Services

### Workflows

This project contains a set of different workflows to show you the different ways a Workflow can be used.

In the project, each file contains different scenarios to show you the common usage of workflows and they are not related to each other.

Remark: This example shows how to use a workflow with multiple roles, and tasks.

Used Technologies: Workflow

Updated on July 8, 2024