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12 Business Rules Examples of Decision Automation in 2024

Updated on Jun 23
4 min read
Written by
Cem Dilmegani
Cem Dilmegani
Cem Dilmegani

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

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Decision management is an emerging field that combines artificial intelligence and business rules to automate complex decision-making processes. A business rule is a formally defined instruction that describes how a certain task should be performed. 

Business rules constitute one of the building blocks of decision management systems, and in this article, we’ll explore 12 examples of how companies use them.

1. Insurance claims processing

Efficient claims processing is crucial for insurance companies as it accounts for nearly 70% of insurance company expenses. However, every claim comes with large amounts of data that must be processed to evaluate the cost of the claim and determine whether the insurer should pay for the insured’s damages.

Establishing business rules and incorporating them into process automation systems can streamline the claims handling processes. Business rules can be used all over a claim’s lifecycle, from the first notice of loss to payment. 

For instance, insurers can use business rules to set minimum requirements for a claim application to be valid as the following: “If X, Y, Z documents are submitted in the specified format, then the insured’s claim application is valid.” This rule can then be incorporated into business systems used for claim intake, which automatically moves insureds to the next step in their claims applications.

2. Insurance underwriting

Underwriting is another essential part of insurance where insurers assess and price risk. However, only 15% of customers are satisfied with the speed of the underwriting process. This is because pricing the risk profitably and fairly requires processing large amounts of data, similar to claims processing.

Insurers can use business rules to determine the applicant’s eligibility and premium. An underwriting AI system can process the provided documents and determine whether the candidate is eligible according to the corresponding business rule.

Then, for instance, it can determine the premium for automobile insurance by using multiple rules: “IF the applicant is under age 40 and male and unmarried and their automobile’s price is under X and it has no accident record, then they qualify for Y coverage.” 

Of course, this is an oversimplification, as underwriting requires large volumes of data to determine the premium. However, business rules engines can process complex rules and optimize decision-making in complex processes. Check our article on underwriting automation to explore more.

3. Loan approval

Like insurance underwriting, lenders can establish business rules to automatically determine whether a loan application is eligible and offer an interest rate according to a set of business rules.

For instance, if an applicant’s credit score is below a threshold, the system would automatically reject the application. Such rules can help lenders to streamline the application review process.

4. Fraud detection

Insurance fraud costs the US more than $40 billion each year, and due to credit card fraud, all customers pay 7 cents more for every $100 due to fraud.

Organizations use machine learning models to detect and prevent fraud and business rules can be employed in ML systems to identify fraudulent transactions or claims and alert staff to take action.

5. Document routing

Routing and tracking documents such as project approval or purchase orders between departments in large companies can be a time-consuming and complex process. Given that an average office worker receives around 120 emails every day and manually routing documents and tracking their approvals is prone to errors.

An intelligent automation system can extract the necessary information from documents and use a business rule to route the documents to the correct person. Moreover, teams can use business rules to automate whether the document needs approval from managers or not. For instance, if the value of the invoice exceeds a specific amount, the system can automatically send the invoice to the manager for approval.

6. Customer discounts

Loyal customers are important for businesses as 86% of them recommend the brand to their friends or family, and existing customers are 50% more likely to try new products than new customers. Therefore, businesses often offer loyalty discounts to retain their loyal customers.

However, manual or semi-manual customer discounts can be a time and effort waste activity. Business rules can be used to offer customers discounts based on sales amount or customer loyalty.

For instance, the rules “If a customer spends more than $2,000 within a month, then offer a 25% discount on their next purchase” or “If a customer shops 5 times within three months, then offer a 15% discount on their next purchase” can be applied during invoice processing.

7. Recommendation systems

Personalized customer experience is getting more important for businesses, as more than 70% of customers expect personalization in the post-pandemic world. Recommendation systems are common tools to provide personalized experiences to customers in e-commerce, retail, banking, and many other sectors.

The accuracy and effectiveness of recommendation systems can be increased by employing business rules. For instance, a business rule can be used to show recommended items whose price doesn’t exceed a specified amount for each displayed product. Another business rule can control which items should be displayed to visitors visiting the company website for the first time.

8. Lead qualification

85% of B2B marketers believe that lead generation is one of the primary challenges they face, as more than 50% of B2B marketers spend more than half of their budget on lead generation. Automating the lead generation and qualification processes can enable your sales team to devote more time to sales. 

For instance, sales teams can establish business rules and determine whether leads are qualified or not. A lead qualification tool integrated with a business rules engine can calculate each lead’s score, discard the ones that are below a certain threshold and determine the approach to reach qualified leads.

9. Compliance

Compliance means adhering to a set of internal or external standards, which can be represented as business rules. Using business rules, audit teams can validate that collected data and documents comply with standards and don’t contain anomalies. Check our articles on intelligent automation in audit and AI in audit for more on audit automation.

10. Pricing optimization

Companies such as retailers, hotels, or airlines use machine learning-based dynamic pricing algorithms to optimize product prices in real-time. These systems leverage data about historical product prices, market trends, customer behavior, or competitors’ prices to maximize profits.

Business rules can be used in dynamic pricing systems to regulate product prices based on the data dynamic pricing algorithm uses.

11. Content redaction

Redaction is necessary to remove or mask sensitive information, such as personally identifiable information (PII), from documents. PII and other personal data are protected by data privacy regulations such as GDPR or CCPA.

There are AI-based automated redaction tools often provided with enterprise content management (ECM) solutions or content services platforms (CSP). These systems can leverage business rules regarding the information that should be removed to identify and remove sensitive information.

12. Employee bonuses

Companies can use business rules for calculating employee bonuses based on various factors such as the company’s profit or the employee’s performance and salary. Implementing business rules to perform bonus calculations can greatly reduce manual efforts and errors.

If you have questions about business rules or how to get started with decision management, we can help:

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Cem Dilmegani
Principal Analyst

Cem is the principal analyst at AIMultiple since 2017. AIMultiple informs hundreds of thousands of businesses (as per Similarweb) including 60% of Fortune 500 every month.

Cem's work focuses on how enterprises can leverage new technologies in AI, automation, cybersecurity(including network security, application security), data collection including web data collection and process intelligence.

Cem's work has been cited by leading global publications including Business Insider, Forbes, Washington Post, global firms like Deloitte, HPE, NGOs like World Economic Forum and supranational organizations like European Commission. You can see more reputable companies and media that referenced AIMultiple.

Cem's hands-on enterprise software experience contributes to the insights that he generates. He oversees AIMultiple benchmarks in dynamic application security testing (DAST), data loss prevention (DLP), email marketing and web data collection. Other AIMultiple industry analysts and tech team support Cem in designing, running and evaluating benchmarks.

Throughout his career, Cem served as a tech consultant, tech buyer and tech entrepreneur. He advised enterprises on their technology decisions at McKinsey & Company and Altman Solon for more than a decade. He also published a McKinsey report on digitalization.

He led technology strategy and procurement of a telco while reporting to the CEO. He has also led commercial growth of deep tech company Hypatos that reached a 7 digit annual recurring revenue and a 9 digit valuation from 0 within 2 years. Cem's work in Hypatos was covered by leading technology publications like TechCrunch and Business Insider.

Cem regularly speaks at international technology conferences. He graduated from Bogazici University as a computer engineer and holds an MBA from Columbia Business School.

Sources:

AIMultiple.com Traffic Analytics, Ranking & Audience, Similarweb.
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Microsoft invests $1 billion in OpenAI to pursue artificial intelligence that’s smarter than we are, Washington Post.
Data management barriers to AI success, Deloitte.
Empowering AI Leadership: AI C-Suite Toolkit, World Economic Forum.
Science, Research and Innovation Performance of the EU, European Commission.
Public-sector digitization: The trillion-dollar challenge, McKinsey & Company.
Hypatos gets $11.8M for a deep learning approach to document processing, TechCrunch.
We got an exclusive look at the pitch deck AI startup Hypatos used to raise $11 million, Business Insider.

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