Principal-Agent Relationship: What It Is, How It Works, and New Developments

The principal-agent relationship is an arrangement in which one entity legally appoints another to act on its behalf. In a principal-agent relationship, the agent acts on behalf of the principal and should not have a conflict of interest in carrying out the act. The relationship between the principal and the agent is called the "agency," and the law of agency establishes guidelines for such a relationship.

This formal relationship is at the center of the financial world. Planners giving advice, fund managers investing your capital, and exchange managers overseeing specific markets are all agents acting on behalf of others. Should the relationship go awry, grave problems often follow. It's thus crucial to understand when investing. More broadly, most modern political arrangements involve principals (voters) and agents (representatives), so it's also a major political concern: how much are the incentives of politicians and those they represent aligned?

Below, we review the basics of this relationship, what regulators and specialists advise to resolve problems in this area, and how new areas of finance arising from machine learning (ML), AI, and other new developments call for rethinking traditional solutions to the problems in this area.

Understanding a Principal-Agent Relationship

A principal-agent relationship is often described as implicitly or explicitly contractual. For example, when investors buy shares of an index fund, they are the principals, and the fund manager is the agent. As an agent, the index fund manager must manage the fund, which consists of many principals' assets, in a way that will maximize returns for a given level of risk following the fund's prospectus.

Any willing and able parties can enter into the principal-agent relationship in a legal transaction. In simple cases, the principal within the relationship is an individual who assigns an agent to carry out a task; other such relationships have principals that include corporations, nonprofits, government agencies, or partnerships.

The agent should be capable of understanding and ultimately carrying out the task assigned by the principal. Common examples of the principal-agent relationship include hiring a contractor to complete a repair on a home, retaining an attorney to perform legal work, or asking an investment advisor to diversify a portfolio of stocks.

Some common examples from finance and investing include the following:

1. Shareholders (principals) and corporate executives (agents)
2. Investors (principals) and fund managers (agents)
3. Bondholders (principals) and company management (agents)
4. Banks (agents) and depositors or regulators (principals)

Fiduciary Relationship

Whether the principal-agent relationship is expressed clearly through a written contract or implied through actions, it creates a fiduciary relationship between the parties involved. This means the agent acting on behalf of the principal must carry out the assigned tasks with the principal's best interest as a priority.

The agent handles tasks the principal gives so long as the principal provides reasonable instruction. In addition, the agent must perform tasks in a manner that will not intentionally harm the principal. A duty of loyalty is also implied within the principal-agent relationship, which requires the agent to refrain from putting himself in a position that creates or encourages conflict between his interest and the principal's interest, also known as the principal-agent problem.

The Principal-Agent Problem

The principal-agent problem arises when one party (the agent) acts on behalf of another (the principal) in a situation where their incentives may not be perfectly aligned and where there is asymmetric information. This concept, which has become central to modern economic and financial theory, emerged from the convergence of two lines of study: the examination of how firms actually operate (not just how economics said they did) and academics exploring risk-sharing among groups.

Origins and Development

The study of the principal-agent problem evolved significantly in the late 20th century. As Ronald Coase noted in his seminal 1937 paper "The Nature of the Firm," economists needed to focus their analysis at the firm level rather than the industry level. This shift led to a deeper examination of individual worker incentives and the conflicts that might arise within organizations.

By the 1970s, scholars such as Stephen Ross, Michael Jensen, and William Meckling had helped create the study of agency theory. Jensen and Meckling published a 1976 paper that gave us the agency definition that most use today and discussed above.

According to agency theorists, or a principal-agent problem to exist, two ingredients are necessary:

1. Conflicting incentives: The interests of the principal and the agent must not be perfectly aligned.
2. Information asymmetry: The agent must have access to information that the principal does not and the principal must be unable to thoroughly monitor the agent's actions. In short, you hire someone and can't always tell what they're doing.

When both these conditions are met, the problem is obvious: the agent has incentives and the ability to act in their interest without immediate accountability.

The financial sector is particularly susceptible to principal-agent problems because of its complex, interconnected nature and the prevalence of risk-sharing arrangements. In financial relationships, agents clearly often have incentives to take actions that benefit themselves at the expense of the principal, such as excessive risk-taking, short-term focus at the expense of long-term value, or misuse of resources.

Trust and Financial Advisors

The above is why trust is central to investors' relationships with financial advisors, lenders, planners, and others. "There’s nothing in life we do, no decision we make, that doesn’t have to do with money in some way," said Valerie R. Leonard, CEO of EverThrive Financial Group in Birmingham, Alabama. "If clients don’t believe they can trust you … they will never do business with you. It’s really that simple."

So, what fosters trust between a client and their financial advisor? "Throughout my career, I’ve recognized that clients must buy into our relationship before they can buy into my services. They need to know that I genuinely care about them, that they can rely on me to do what I say I’m going to do, and that I’m willing to be open and honest about who I am," Leonard said. 

We'll see how this model faces tests once ML and AI are involved.

Challenges and Solutions

Addressing principal-agent problems, besides the work of building trust, often involves finding creative ways to align incentives and reduce information asymmetry. Standard approaches include the following:

1. Performance-based compensation
2. Behavioral finance training to better align incentives
3. Increased monitoring and reporting requirements
4. Governance structures that provide oversight
5. Laws and regulations that enforce fiduciary duties, that is, provide grave sanctions to counteract profit-seeking incentives among agents

You'll notice that many of the above have come about in the years since agency theory was introduced. You'll notice, too, that these solutions aim to do more than bolster the trust of clients; they are supposed to work when there is none at all.

As financial markets and instruments become increasingly complex, new challenges in managing principal-agent problems continue to emerge, requiring a rethinking of these traditional approaches.

New Developments in the Principal-Agent Relationship

Recent advances in AI and ML are reshaping the landscape of principal-agent relationships in finance, introducing novel challenges for regulators and clients. While ML-based trading systems have received significant attention, broader AI applications and regulatory shifts are also transforming how financial institutions operate and are governed.

Machine Learning and Automated Trading

As researchers have noted in recent years, ML-based trading systems are reconfiguring traditional principal-agent relationships. These systems use complex mechanisms like deep neural networks to develop trading rules internally based on data inputs. This shift introduces new forms of knowledge risk (how much does the agent know about what the principal is doing?), limits how the principal can change trading when directed by the agent, and obscures the decision rules used by automated systems.

Essentially, previously, to address how incentives between principals and agents often diverge, specialists have suggested more robust contracts to cover issues that might arise or better regulations in this area. These solutions don't really apply in these new spaces. As the economic sociologist Christian Borsch puts it, "Conventional solutions to principal-agent problems can't easily address [the issues with ML systems]. For example, given the opacity of these systems, better-designed contracts between the principal and agent or more deterministically construed relationships [that is, computer programmers directing applications] between them offer no viable solutions."

As is well known, not even computer scientists who develop AI and deep neural networks know precisely how the algorithmic processes arrive at specific decisions or results. For now, investors have their concerns: three-quarters (74%) of retail investors trust human advice over robo-led advice.

The issue is "jarring enough," as Borsch puts it regarding the issues raised in finance, but ML systems are also being applied in medicine, where principal-agent relationship issues also arise.

Broader AI Applications in Finance

Beyond trading, AI has been deployed across various financial services, each with its own principal-agent implications:

• Credit scoring and lending: AI algorithms are increasingly used in credit decisions, potentially reducing human bias but also raising concerns about transparency and fairness in lending practices—or having the appearance of objectivity in the ML system was trained on structurally biased data.
• Fraud detection: AI systems are enhancing the ability to detect financial fraud, but their effectiveness relies on the quality of data and the alignment of incentives between financial institutions and their technology providers.
• Robo-advisors: AI-powered investment advisory services are changing the relationship between financial advisors and clients. These systems promise more objective, data-driven advice but raise questions about fiduciary responsibility and the role of human judgment.

Blockchain and Decentralized Finance (DeFi)

Like ML, the rise of blockchain technology and DeFi platforms are said to introduce new issues and potential solutions for classic principal-agent relationships:

• Smart contracts: These self-executing contracts have the terms directly written into code and supposedly cut the need for intermediaries in some financial transactions, potentially minimizing certain types of agency conflicts.
• Decentralized autonomous organizations: These blockchain-based entities operate through community governance, presenting a novel approach to aligning stakeholder interests.

Regulatory Responses and "Ethical AI"

The rise of AI in finance has prompted regulators to reconsider how oversight is done:

• Explainable AI: Regulators and researchers emphasize the need for "explainable AI" in financial services that would enable institutions to interpret and justify AI-driven decisions. This push addresses the "black box" problem inherent in many complex AI systems. However, it's one thing to understand that there's a problem; thus far, it's been quite another to render AI explainable—it's a black box problem after all—especially when financial institutions ill-suited to understanding such systems are involved.
• AI governance: Financial institutions are developing internal governance specifically for AI deployment, aiming to align AI systems with organizational values and regulations.
• Ethical AI principles: There's a growing focus on incorporating ethical considerations into AI development and deployment in finance, addressing issues like bias, fairness, and societal impact.

Changing Compensation Structures

The financial industry is also seeing shifts in how human agents are compensated:

• Longer-term incentives: In response to criticisms of "short-termism"—that agents are often given greater incentives to pump up short-term results—some institutions are changing how agents are compensated to better align rewards with longer-term performance metrics.
• Environmental, social, and governance (ESG)-linked incentives: ESG factors are often now incorporated into executive compensation packages, aligning management incentives with broader societal goals.
• Team-based incentives: Recognition of the collaborative nature of modern finance is leading to more team-based compensation structures, potentially mitigating some of the individual-level agency problems.

The Bottom Line

The principal-agent relationship is a fundamental concept in finance and economics that occurs when one party (the agent) acts on behalf of another (the principal). This arrangement is ubiquitous in the financial world, from corporate executives managing companies for shareholders to fund managers investing on behalf of clients. While these relationships are essential for the functioning of modern finance, they have the inherent potential for conflicts of interest. The principal-agent problem arises when the agent's incentives don't perfectly align with those of the principal, and when the principal can't fully monitor or understand the agent's actions.

Recent technological advances, particularly in AI and ML, are reshaping these relationships in profound ways. As decision-making becomes increasingly automated and opaque, traditional methods of aligning incentives and ensuring accountability are being challenged.