Navigating the Future of Decision Management - Rules Engines vs. Machine Learning

PRZEMEK HERTEL
March 28, 2024
Blog

Slow decisions cost you money.

Poor decisions cost you money. 

Time spent on meetings, that you should not participate in, cost you money.

What if you could save all this money? 

What if your organization could adapt to changes swiftly, seamlessly, and make data-driven decisions automatically?

What if you could predict the future and plan the decisions?

What if your committee presence was determined beforehand based on data showing if you're needed or not?

It's all possible with the combination of business rules engines and machine learning.

Business Process Automation – Why is It Important For Your Business?

Healthcare executive sat three times on the same 90-minute proposal committee, because no one knew who should make the call. 

A pharma company lost an acquisition deal because of the hesitation. 

McKinsey & Company estimated that 530,000 days of managers' time from a Fortune 500 company, an equivalent of $250 million in wages, is wasted each year

McKinsey further states, that executives spend nearly 40% of their time making decisions, with 61% saying, that half of this time is spent ineffective. Actually, companies waste $37 billion every year on ineffective meetings.

A research by Bain & Company showcases, that an average organization loses 20% of its operational capacity on, as they call it, "organizational drag" – structures and processes that take time and hold people from doing the actual work. 71% of C-level executives already decentralized decision-making or are planning to do it.

There is compelling evidence linking business success to data-driven decision-making. Nowadays, nearly every aspect of a business can be quantified and analyzed, leading many decision-makers to leverage insights from available data automation. Employing various technologies such as data mining, machine learning (ML), and deep learning (DL), companies hire specialists like data analysts and scientists to apply these methods. With faster information processing methods, reliance on individuals diminishes while organizations' information capacity expands.

Rule Engines and Machine Learning – How do They Work?

Rules engines, embodying the essence of business logic, operate on the principle of determinism. By applying predefined rules to a given set of business processes, they ensure decisions are made with transparency and consistency. This enhances the decision-making process and simplifies updates to the business rules management system, keeping pace with ever-evolving market dynamics and regulatory demands.

Machine learning, on the other hand, brings a dynamic edge to decision management systems. It thrives on the ability to digest and learn from vast amounts of historical and customer data, sharpening its predictive analytics capability over time. This continuous learning process equips decision management solutions with the foresight to anticipate future outcomes, thereby optimizing operational decisions and enhancing customer satisfaction.

Machine Learning algorithms have the benefit of:

  • Data Handling: Organizations collect massive amounts of data, but analyzing it effectively can be overwhelming. ML algorithms can process this data swiftly and accurately, identifying patterns and trends that humans might miss.
  • Predictive Power: By learning from historical data, ML models can forecast future outcomes. This allows organizations to anticipate customer behavior, market shifts, and potential risks, enabling proactive decision-making.
  • Objectivity: Human decision-making can be susceptible to biases. ML algorithms, however, analyze data objectively, reducing the influence of personal biases and promoting fairer choices.

The beauty of integrating machine learning with rules engines in decision management lies in their complementary strengths. While rules engines bring structure and clarity to business operations, machine learning injects adaptability, ensuring the business stays aligned with changing market dynamics and customer behaviors. Together, they form a formidable duo that automates decision-making processes, making data-driven decisions a reality and propelling businesses toward unparalleled efficiency and innovation.

You could say that business rules engines answer the question: "What to do?", while machine learning gives an answer to: "Why should we do it?".

Business Rule Engines in Decision Management

Business rules engines streamline decision-making by automating actions based on predefined rules.  Think of them as software tools that enforce the “what ifs” and “should dos” within your organization.  Here's how they play their role:

  • Automating decision-making, applying predefined rules to data to produce outcomes.
  • Ensuring consistency across all decisions by strictly following the set rules.
  • Facilitating easy updates to business logic without needing to alter the underlying application code.
  • Integrating with existing IT infrastructure to streamline processes.

Benefits of Rules Engines Implementation

High executives don't waste time making the same decisions over and over, as the decision has been already made, and unless the overall company policy doesn't change, the decision stays the same.

"If X happens, Y is done".

  • Improved efficiency by automating routine decisions, freeing up resources for more complex tasks.
  • Increased agility, allowing businesses to quickly adapt to changes in regulations or market conditions.
  • Enhanced accuracy and consistency in decision-making, reducing human error and bias.
  • Better compliance with regulations and internal policies, as decisions are made following predefined rules.

Machine Learning in Decision Management

What about tough calls? How many executives wait with their decisions or make a poor choice under pressure?

PwC reports of an automotive company which used a dynamic agent-based model to simulate numerous strategic scenarios for entering the ridesharing market. This model enabled decision-makers to experiment with different policy setups in a virtual, risk-free environment, providing insights into long-term market share and revenue outcomes before committing to any decisions.

Benefits of using Machine Learning in Decision Making

In the finance and insurance sectors, machine learning (ML) has been effectively applied to revolutionize traditional processes, enhance decision-making, and deliver tailored services to customers. Here are some finance and insurance-related examples of ML applications:

  • Predictive Analytics in Healthcare: Healthcare organizations leverage machine learning to analyze patient records and predict admissions, enhancing patient care and reducing costs. For example, predictive models forecast patient admissions, enabling efficient resource allocation and the implementation of targeted care plans​​.
  • Efficiency in Energy and Utilities: Energy companies use machine learning to anticipate equipment failures and energy demands, ensuring reliability and optimizing resource allocation​​.
  • Data-Driven Insights in Retail: Machine learning helps predict consumer behavior, allowing retailers to personalize marketing efforts and optimize supply chains for increased customer satisfaction​​​​.
  • Adaptability in Legal Outcomes: Natural language processing, a subset of machine learning, predicts court case outcomes with significant accuracy, potentially aiding in creating a more equitable justice system​​.
  • Efficiency and Speed in Email Filtering: average employee wastes 28% of the time daily sifting through the mailbox. Machine learning streamlines email management by effectively filtering spam, showcasing its capability to automate and expedite mundane tasks​​.
  • Risk Management in Finance: Machine learning is pivotal in detecting fraudulent financial transactions in real-time, showcasing its role in enhancing cybersecurity and preventing financial losses​​. E. g. Mastercard uses machine learning to analyze purchase locations, time of day, and purchase types to flag potentially fraudulent activity. JP Morgan Chase implemented machine learning to detect fraud.

Rule Engines vs Machine Learning: Friend or Foe?

Machine learning and business rules engines are like partners in the dance of decision-making. Machine learning offers the dynamism of learning from data, predicting outcomes, and adapting over time. Business rules engines provide the structure, ensuring decisions align with company policies and regulations. 

When they work together, organizations harness the power of both worlds: the adaptability and insight of machine learning with the consistency and reliability of rule-based decision-making. It's a match that allows for sophisticated, yet governed, organizational agility and smarter decisions, without stepping on each other's toes.

Feature Machine Learning (ML) Business Rules Engine (BRE)
Approach Learns from data to identify patterns and make predictions Executes pre-defined rules to automate decisions
Data Dependence Requires large amounts of historical data for training models Relies on human expertise to define clear business rules
Flexibility Adapts to changing situations as it learns from new data Requires manual updates to the rule base as business needs evolve
Transparency Decision-making process can be complex and opaque ("black box") Logic behind decisions is clear and transparent
Strengths * Complex & dynamic situations * Identifying hidden patterns * Adapting to changing environments * Automating routine decisions * Ensuring consistency * Transparency & auditability
Best Used For * Fraud detection * Customer behavior prediction * Risk management * Loan approvals * Eligibility checks
Complementary With BREs (refine rules based on ML insights) ML (improve decision automation with clear rules)

In our view, it's not the case of "Machine Learning or Rules Engines". It's a case of "How to integrate them to maximize the output?". Combining data analytics and predictions from machine learning with seamless decision making from business rules engines can give you an unfair competitive advantage.

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