Knowledge engineering

What Is Knowledge Engineering?

Knowledge engineering is a discipline focused on designing, developing, and maintaining knowledge-based systems, which are a core component of artificial intelligence (AI) applications. Within the broader field of financial technology, it involves extracting, formalizing, and representing human expertise and domain-specific knowledge in a structured format that machines can process and utilize. This process is crucial for building intelligent systems capable of performing tasks that typically require human intelligence, such as problem-solving, decision-making, and understanding complex data. Knowledge engineering plays a vital role in transforming raw data analysis into actionable insights, particularly in sophisticated environments like financial markets.

History and Origin

The origins of knowledge engineering are deeply intertwined with the early development of artificial intelligence, particularly the rise of expert systems in the 1970s and 1980s. These systems aimed to replicate the decision-making capabilities of human experts within specific domains by encoding their knowledge into a computer program. Early applications of AI in finance began to gain prominence in the 1980s with the commercialization of expert systems. For instance, Applied Expert Systems (APEX) released PlanPowerOne in 1986, one of the first commercially shipped expert systems designed to assist with personalized financial planning for individuals.⁹ This era saw a significant push to leverage knowledge-based systems for tasks like predicting market trends, credit scoring, and providing customized financial advice, driven by the desire to reduce human error and enhance efficiency.⁸,⁷ The focus was on formalizing human intuition and experience into a machine-readable format, laying the groundwork for more advanced AI applications that would emerge later.

Key Takeaways

• Knowledge engineering is the process of extracting, representing, and applying human expertise for machine use.
• It is fundamental to the development of knowledge-based systems and expert systems within artificial intelligence.
• In finance, knowledge engineering supports applications like fraud detection, credit assessment, and financial planning.
• Challenges include the complexity of human knowledge, data quality, and the need for continuous model refinement.
• It enables machines to make informed investment decisions and provide analytical support.

Interpreting Knowledge Engineering

Knowledge engineering is interpreted through the effectiveness and accuracy of the intelligent systems it helps create. A well-engineered knowledge base should enable a system to provide consistent, logical, and accurate outputs for complex problems within its defined domain. For example, in risk management, the interpretation revolves around how effectively the system can identify, assess, and mitigate various financial risks based on the formalized knowledge of risk experts. If a system developed through knowledge engineering consistently identifies high-risk scenarios and suggests appropriate countermeasures, it indicates a successful interpretation and application of the underlying knowledge. Its value is also seen in its ability to support and enhance human capabilities, rather than fully replace them, providing a robust decision support system.

Hypothetical Example

Consider a hypothetical financial institution, "Global Wealth Advisors," that wants to improve its automated credit scoring process using knowledge engineering. Currently, loan officers manually assess creditworthiness based on various factors like income, debt-to-income ratio, and credit history, applying their accumulated expertise.

To implement knowledge engineering, Global Wealth Advisors would:

1. Knowledge Acquisition: Interview their most experienced loan officers to understand their rules of thumb, heuristics, and qualitative judgments when evaluating loan applications. This might include, "If a client has a stable employment history for more than five years and a debt-to-income ratio below 30%, they are generally low-risk, unless they have recent bankruptcies."
2. Knowledge Representation: Translate these informal rules into a formal structure, such as "if-then" rules or semantic networks, that a computer program can understand.
   • IF (Employment_History = "Stable" AND Years_Employed >= 5)
   • AND (Debt_to_Income_Ratio < 0.30)
   • AND (No_Recent_Bankruptcies = TRUE)
   • THEN (Credit_Risk = "Low")
3. Knowledge Validation: Test the system with historical loan data to ensure its decisions align with expert judgments and actual loan performance. This iterative process helps refine the rules and identify gaps.
4. Knowledge Deployment: Integrate this knowledge-based system into their loan application platform, allowing for more consistent and faster initial credit assessments, which loan officers can then review. This automation frees up the loan officers to focus on more complex or unusual cases.

Through knowledge engineering, Global Wealth Advisors creates a structured, shareable, and scalable repository of its lending expertise.

Practical Applications

Knowledge engineering has several practical applications within the financial sector, enabling more intelligent and efficient operations:

• Fraud Detection: By formalizing the patterns and indicators of fraudulent activities that human experts recognize, knowledge engineering helps develop systems that can analyze large volumes of transactional data in real-time to flag suspicious behaviors.⁶
• Algorithmic Trading: Knowledge engineering contributes to the design of algorithmic trading systems by encoding market rules, investor strategies, and economic indicators. These systems can then execute trades based on predefined conditions and learned market behaviors.
• Robo-Advising: In wealth management, knowledge engineering is used to build robo-advisors that provide automated financial advice. These systems capture expert knowledge regarding portfolio construction, risk tolerance assessment, and tax optimization to offer personalized recommendations.
• Compliance and Regulation: Financial institutions use knowledge engineering to build systems that interpret and apply complex regulatory frameworks, ensuring compliance with evolving laws and internal policies. Banks are increasingly embracing AI to boost productivity in areas like coding and daily task automation.⁵ The Federal Reserve has also noted the potential of AI to reshape the financial services industry, while emphasizing the need for regulatory frameworks to respond appropriately.⁴

Limitations and Criticisms

Despite its benefits, knowledge engineering faces several limitations and criticisms, particularly concerning the inherent complexity of human expertise and the dynamic nature of financial markets.

One primary challenge is the "knowledge acquisition bottleneck." Extracting comprehensive and accurate knowledge from human experts can be time-consuming, expensive, and often incomplete, as much human expertise is tacit or difficult to articulate explicitly. Furthermore, the knowledge encoded through knowledge engineering is typically static and rule-based, making it less adaptable to rapidly changing market conditions or unforeseen events compared to approaches like machine learning, which can learn directly from data.

Critics also point to the "brittleness" of knowledge-based systems; they perform well within their defined domain but fail spectacularly when encountering situations outside their programmed knowledge. This can lead to erroneous financial decisions or missed opportunities. The International Monetary Fund (IMF) has highlighted several risks associated with AI in finance, including embedded bias in models and the potential for "hallucinations" (producing false but plausible outputs), which are relevant to how knowledge is initially captured and represented.³,² Ensuring the explainability and transparency of decisions made by knowledge-based systems remains a significant challenge, especially as regulatory scrutiny on AI in financial services increases.¹

Knowledge Engineering vs. Artificial Intelligence

While often used interchangeably by the public, knowledge engineering is a specific sub-field and methodology within the broader discipline of artificial intelligence. Artificial intelligence encompasses the entire spectrum of creating machines that can simulate human intelligence, including areas like machine learning, natural language processing, computer vision, and robotics.

Knowledge engineering, in contrast, focuses specifically on the process of capturing, representing, and reasoning with explicit human knowledge. It is particularly associated with symbolic AI and the development of expert systems that operate based on predefined rules and facts. While modern AI often leverages statistical and data-driven methods like neural networks (a component of predictive analytics), knowledge engineering emphasizes a more explicit, structured approach to encoding intelligence derived from human expertise. The primary distinction lies in their scope: AI is the overarching goal of intelligent machines, while knowledge engineering is a set of techniques and practices to achieve that goal, particularly through the formalization of human understanding.

FAQs

What is the main goal of knowledge engineering?

The main goal of knowledge engineering is to enable intelligent systems to emulate human expertise and decision-making by systematically acquiring, representing, and applying domain-specific knowledge. This process makes human knowledge accessible and usable by machines for various tasks.

How does knowledge engineering benefit financial institutions?

Knowledge engineering benefits financial institutions by enhancing operational efficiency, improving decision-making, and automating complex processes. It supports applications in fraud detection, credit assessment, portfolio management, and compliance, helping reduce human error and expedite analyses.

Is knowledge engineering still relevant with the rise of machine learning?

Yes, knowledge engineering remains relevant, even with the rise of machine learning. While machine learning excels at finding patterns in large datasets, knowledge engineering is crucial for incorporating explicit human expertise, common-sense reasoning, and domain constraints that data-driven models might miss. Often, a hybrid approach combining both is used to build robust financial models.

What are the challenges in knowledge acquisition?

Challenges in knowledge acquisition include the difficulty of extracting tacit knowledge from experts, the inconsistency or incompleteness of human knowledge, and the time-consuming nature of the process. Experts may find it hard to articulate their decision-making processes, leading to a bottleneck in system development.

Can knowledge engineering systems adapt to new information?

Traditional knowledge engineering systems, particularly rule-based ones, are less adaptable to new information unless their knowledge bases are manually updated. However, advanced systems may integrate components that allow for some degree of learning or dynamic updates, combining the structured knowledge with adaptive mechanisms.