Political Economy of Elder Financial Exploitation

 

Political Economy of Elder Financial Exploitation

Nicholas Djoulia

May 2026

Abstract

Since the dawn of the digital age, financial fraud targeting senior citizens has been one of the most significant yet underrepresented economic and political issues in American life. In 2024 alone, $81.5 billion was lost, with a potential $137 billion when accounting for underestimation. With these figures, elder financial exploitation (EFE) has shifted from small-scale, individual risks to systemic economic risks, posing a significant threat to our financial institutions. This paper delves into that fact that EFE must be reframed as a broad market failure accelerated by these main drivers: the concentration of wealth in a technologically vulnerable generation, deception of this generation through artificial intelligence (AI), and lack of regulatory framework constitutionally and its inability to combat the harm being done; this is a full shift away from the issue being framed as a financial literacy problem that can be quickly amended through a change in curriculums for elders. Through neuropsychological research on vulnerability cognitively, macroeconomic analysis of wealth distribution and aging, FinCEN (Financial Crimes Enforcement Network) Bank Secrecy Act implementation data, behavioral economics of fraud, and regulatory fragmentation, the paper explores the issue of elder fraud online through a multi-factor approach, even with a growing awareness of the issue. The paper will analyze the regulatory framework governing EFE, evaluate pending legislation, and identify the key issues in government that must be resolved for reform to be possible.

1. Introduction

Imagine receiving a call from your grandchild. They had just been in a car accident and needed money to cover the repairs, but were too afraid to ask their parents. They sound just like they always have, kind and patient, and they have always been honest with you. You rush towards your computer and immediately wire the money. A few days later, you call your grandchild, and you discover that the car accident did not, in fact, happen. That is when you discover that you have been taken advantage of financially by an online scammer, who used AI to generate your grandchild’s voice, utilizing a clip from their Snapchat story. You have just joined thousands of people your age who have been financially scammed online.

For many, this is not a made-up scenario. This is a real phenomenon and a real method used by scammers to steal elders’ wealth; note that this is a fairly new occurrence in the online world. For most of the digital age, EFE occurred on a small scale, and it was therefore containable through law enforcement and regulatory frameworks. Scammers would send bulk phishing emails filled with spelling errors, posing as Middle Eastern princes, and target older individuals with limited technology experience. However, around 2022, generative AI completely altered the landscape of online scams. Committing fraud online became cheaper and easier, and many scammers framed their actions as legitimate communication. The overall result was a crisis that governments and law enforcement, through now-outdated regulatory and policy frameworks, were unable to handle properly. 

With $81.5 billion being lost to online scams in 2024 alone for Americans over 60, a number that could be as high as $137 billion, taking into account underestimates (FTC, 2024). That figure is directly comparable to that of the GDPs of entire countries. However, the real problem is that the response to this issue has been slow in terms of regulatory action and policy initiatives, even though advocacy campaigns and gradual BSA (Bank Secrecy Act- Focuses on anti-money laundering) amendments have been implemented. 

Taking into account the scale of this issue, the central argument of this paper is that the misalignment between the EFE problem and response is not a political failure, but a failure to understand the issue in today’s political economy properly. EFE is still being treated as an issue of individual consumers that could be fixed with changes to financial literacy curricula. This paper argues that this issue is an inherent and systemic market failure driven by the accumulation of elders’ wealth, the growth of AI-fueled scamming mechanisms, and an aging regulatory framework that is too uncoordinated to address it on a broad scale properly. Understanding this updated identification is the right approach to creating the policy necessary to prompt meaningful change eventually.

2. The Economics of an Aging America

1. Wealth Concentration and the Longevity Transition

To understand why EFE has festered into a systematic market issue, it is crucial first to understand where people’s wealth is concentrated. Modigliani and Brumberg’s (1954) life cycle hypothesis posits that people save money during their working years and access that money to spend during their retirement years. The result of this hypothesis is that wealth accumulation peaks for most just before retirement, and, as a result, most of the wealth in the US population rests with older adults. In 2025, 1 in 11 people worldwide is over the age of 65, and by 2050, that number is projected to double (UN DESA, 2022). In the US only, the ratio of retirees to working people is set to rise from 28.5% in 2025 to nearly 38% by 2050 (McKinsey & Co., 2026). 

This is not just a small shift within the population; it is a massive economic one. As Baby Boomers continue to age and retire, the nation's wealth and assets rest in the hands of older Americans. This is what many are calling the Great Wealth Transfer. By 2040, 22% of the US population will be in their 60s, reaching retirement age, which is a great increase from 12% in 2005 (McKinsey & Co., 2026). Economist Kim (2025) finds that wealthier retirees continue to save and accumulate wealth even after retirement, meaning that the concentration of wealth in this demographic is likely far greater than estimates and economic models suggest. What all of this means is that there has never been a time when so much of the nation’s wealth is concentrated in the hands of older generations. Those generations are being forced to connect with an increasingly digital world that they did not have access to growing up. 

2. What Fraud Actually Costs the Economy

When fraud dislocates funds from elders, they are not transferred and tracked as they would be with bank transfers; instead, these funds mostly disappear from the domestic economy. Fraudulent funds, usually making their way to Transnational criminal organizations, are funneled overseas through offshore accounts and cryptocurrency exchanges, permanently removed from domestic economic cycles. Applying Keynesian economics to this situation, removing $81.5 billion annually from the spending budgets of older Americans has significant impacts on the healthcare, housing, and service sectors of the economy that depend on elder consumer spending.

In addition to the effects on specific individuals and economic sectors, there is a major impact on fiscal costs, as the savings of senior citizens decrease due to fraud, while applications for Medicaid, housing, and Supplemental Security Income increase, thereby placing a greater financial burden on the government to aid this population. To note, converting private losses to governmental expenditures without raising tax revenue is a fiscal nightmare for both state and federal governments (AARP Public Policy Institute, 2023). The majority of EFE loss data is based solely on direct financial harm to seniors, not on the broader economic and regulatory impacts. The AARP (2023) conservatively estimates that annual direct losses to elderly Americans from online fraud are $28.3 billion. The number balloons much higher, accounting for the true welfare cost of online fraud, including increased government spending and strain on Medicare and Medicaid.

Table 1

Macroeconomic Cost Profiles of Elder Financial Exploitation (2024–2026)

---------------------------------------------------------------------------------

Metric Class               Value (Annualized)   Primary Target / Economic Outcome

---------------------------------------------------------------------------------

Confirmed FTC Losses       $10.1B – $81.5B      Direct asset drainage from 

                                                elder accounts (Ages 60+)

                                                

Estimated True Welfare     $137.0 Billion       Accounts for severe systemic 

Cost                                            unreporting and shame-biases

                                                

FinCEN Suspicious          $27.0 Billion        Interdicted or flagged institutional 

Activity (SARs)                                 transaction velocity

                                                

Long-Term Capital Flight   $75.0 Billion        Cumulative 4-year drainage via 

                                                pig-butchering networks

                                                

Diversion to Public        Multi-Billion        Conversion of private equity 

Safety Nets                (Case Strain)        to Medicaid and SSI expenditures

---------------------------------------------------------------------------------

Sources: Federal Trade Commission (2024); FinCEN (2024a); Griffin & Mei (2024).

3. How Compliance Costs Are Reshaping Banking

EFE is also creating negative institutional impacts within the banking sector in ways that induce further financial risk for seniors. Under the Bank Secrecy Act, FinCEN receives over 155,000 reports related to EFE annually, with approximately $27 billion in suspicious activity reported (FinCEN, 2024a). The record $1.3 billion penalty against TD Bank for Anti-Money Laundering (AML) failures in 2024 prompted institutions to increase spending to combat this (FinCEN, 2024b). This financial burden caused by suspicious financial activity falls primarily on small banks. 

The interesting point is that small banks have historically been best at combating EFE through connected relationships with the community, rather than through complex online safeguards such as algorithms. Local bank employees who notice unusual activity in an elder’s account, such as large and frequent cash withdrawals, would likely recognize that the elder was being scammed and financially taken advantage of. As compliance costs for preventing EFE grow, including staff training and technological infrastructure investments, smaller banks are being driven out of the elder financial market, with human detection of fraud slowly being eliminated (Gibson Dunn, 2026). By regulation, in turn, forcing expensive, algorithm-based compliance requirements on the banking sector to prevent fraud, this issue is further exacerbated, leading to financial uncertainty for elders as smaller banks are no longer able to adequately accommodate their needs (due to the decline of human-driven fraud detection).

Figure 1

Operational Flow of the Banking Compliance Paradox in EFE Mitigation

1. Regulatory Enforcement Level

   Severe FinCEN and BSA monetary penalties force multi-million dollar institutional 

   compliance overhauls.

2. Institutional Reaction

   Small and community banks face disproportionate capital strain under structural 

   compliance overhead.

3. Operational Transition

   Financial institutions phase out localized, human-driven "relationship banking" 

   models in favor of centralized, automated transaction monitoring.

4. Technological Deployment

   Rigid transaction filters and automated algorithmic red flags are introduced 

   to monitor accounts.

5. Systemic Outcome

   The automated framework systematically misses interpersonal, close-relationship 

   exploitation while disproportionately freezing legitimate transactions or excluding 

   vulnerable elderly account holders from access.

III. How AI Changed Elder Fraud

1. From Phishing Emails to Precision Targeting

Building on the sentiments expressed in the previous section, the fraud game, through technological advancement, is massively disrupting the ability of existing infrastructure to combat the issue. For most of the digital age, elder fraud was committed through mass, uncoordinated efforts. Scammers would send thousands of phishing emails to seniors, with slim hopes of a response, but these letters were poorly written and easy for many to detect. However, this trend in online financial scams has been effectively curbed by AI. Since late 2022, generative AI has enabled scammers to personalize and target victims individually, through deepfake phone calls, increasingly realistic emails, and tech support pop-ups. By using social media profiles, obituary records, and free online databases, scammers can access senior citizens’ financial data by building “credible” connections with vulnerable seniors through generative AI (Sumsub, 2024). 

The data reflects this dangerous phenomenon. Cryptocurrency wallets linked to scam activity gained $9.9 billion in 2024, a figure that is bound to grow. By 2025, total scam-related crypto losses were estimated at $17 billion, with deepfake and impersonation scams growing 1,400% from the previous year. Per victim, average payments grew from $782 to $2,764, a 253% increase. This shows AI’s growing capabilities in maintaining connections with senior-citizen victims before protective services, such as a VPN, or the victims themselves can spot fraud (Chainalysis, 2025; 2026). A particular AI-powered scam method, deepfakes, grew from 500,000 reported cases in 2023 to over 8 million in 2025 (Resemble AI, 2025). With voice cloning, particularly of family members or government officials, online requiring 3 seconds of audio, deepfakes are a highly dangerous and financially concerning scam technique that is concerning for governments, who mostly have ineffective and outdated governmental regulations to tackle the issue.

Figure 2

Escalation Curve of Deepfake Scam Metrics (2023–2025)

Reported Cases 

(In Millions)

  8.0 |                                                        * (2025: 8.0M)

      |                                                      .

  6.0 |                                                    .

      |                                                  .

  4.0 |                                                .

      |                                              .

  2.0 |                                        .

      |                             .

  0.5 |  * (2023: 0.5M)        .

      |______________________________________________________________________

         2023                       2024                       2025

                                                                    Timeline

Source: Resemble AI (2025).

2. Pig-Butchering: Patience as a Fraud Strategy

The most significant and growing AI-powered scam technique used in EFE is no longer just deepfakes; it now includes the pig-butchering scheme, also classified as a romance scam. Deriving its name from inhumanely fattening up animals before slaughter, these scams work by scammers spending days and weeks establishing connections and relationships with seniors through social media and messaging apps. Scammers eventually lead unsuspecting seniors to fraudulent investment opportunities, primarily in cryptocurrency, from which they can extract funds. In contrast with traditional online scam methods, the pig-butchering technique is slow-moving and emotionally driven. Scammers take advantage of the fact that seniors may experience loneliness towards the end of their lives and are therefore more likely to enter online “relationships” with scammers (DeLiema, 2018). 

The financial scale of this problem is extraordinary and frightening for victims and regulators. The University of Texas’ study of blockchain transactions found that pig-butchering networks displaced $75 billion of senior-citizen wealth into faulty crypto investments over 4 years, between 2020 and 2024 (Griffin & Mei, 2024). Many of these transactions are facilitated by Organized and Transnational crime organizations using cheap and trafficked labor from Asia. This means that EFE and human trafficking are innately interlinked, and that ignoring one also negatively impacts the other (INTERPOL, 2025). From an international perspective, domestic regulatory action is largely unable to solve this problem. 

3. The Double-Edged Sword of AI Governance 

At the center of the AI-era fraud policy is the fact that AI-powered scams are countered by AI-powered countermeasures, including LLMs (large language models) and deepfakes. Regulatory efforts that use AI-powered detection methods to restrict AI-powered scams can have a positive impact on scammers themselves while undermining overall defense efforts. Criminal networks and organized crime groups are not bound by regulatory efforts, but a bank’s detection systems are. This means that AI regulatory policy can backfire on regulators, inhibiting the efforts of entities that protect against scams and giving scammers an undue advantage.

IV. Why Senior Citizens Are Structurally Vulnerable

1. What the Brain Science Shows

Seniors' vulnerability to financial fraud is not a matter of lacking care or attention to detail. It can instead be rooted in biology. As shown in research, older adults experience a steady cognitive decline, with thinning of brain structures that help regulate how we process and respond to threats (Fenton et al., 2022). This biological phenomenon, in contrast to normal adult cognitive function, predisposes seniors to vulnerability in the digital age.

In terms of the policy implications of biology, timing is crucial. Work studied by Frontiers in Psychology (2025) uncovered that people with a later diagnosis of Alzheimer’s missed bill payments around 6 years before their diagnosis, and as a result suffered credit declines about 2.5 years before their diagnosis. This shows that EFE vulnerability can be directly linked to declines in cognitive capacity, memory, and executive functioning in older adults, before any formal diagnosis or treatment. This means that regulators and governments, on a systemic level, need to implement safeguards for older adults, not just for 65+ individuals, to combat the decline in cognitive ability early on, and therefore help these individuals be better protected against online financial scams.

2. Social Isolation and the Trust Trap

Cognitive vulnerability is not just a standalone biological issue but one that is shaped by social developments and societal circumstances facing older adults. In many societies, it is common that older adults face increased isolation and loneliness, and therefore can face conditions such as depression and MCI (Mild Cognitive Impairment). DeLiema (2018) links this knowledge to EFE, in that fraud succeeds when scammers target older individuals with these conditions. Mixed with the fact that seniors face a technologically advancing world, shrinking social connections, and less contact with family members in terms of their finances, the risk of exploitation is raised significantly.

The full picture of this problem is further impacted by who, in fact, commits the fraud. FinCEN (2024a) shows that 40% of exploitation involving trusted relationships involves the elder’s children. Betrayal trauma theory can therefore be linked to this to explain why this form of EFE is unexpected and greatly damaging: through trusted individuals, caregivers, and relatives exploiting seniors, greater financial harm is induced, and psychological welfare is more damaged than if the senior had suffered scams at the hands of a stranger. The cognitive mechanism people use to function in relationships has a difficult time identifying and accepting instances of abuse in close relationships, and this further worsens the psychological and subsequent financial effects of this form of exploitation. Overall, the institutional deficiency here is that standard regulatory action and fraud awareness campaigns typically do not account for this form of scam at all, posing an even greater risk to potential victims. 

3. Why Financial Education is Not Enough

The instinct of many is to address this deep-rooted issue regarding EFE by enhancing financial literacy programs to teach about it. However, the evidence does not support this method as a practical and effective policy solution. Hancock, Czaja, and Beach (2025) confirm that financial knowledge and aptitude do not reduce the risk of online financial scams; social advocacy and support for seniors is far more effective to help not only prevent financial loss but also protect their cognitive welfare in the long run, as we see solutions, contrary to the belief of some, are not posited by financial literacy programs. Burke et al. (2022) ran a randomized trial of fraud education programs and interventions, and the results confirmed that, overall, amendments to financial literacy programs do not sustain lasting behavioral effects for seniors (in terms of their capacity to prevent their victimization by fraud). 

Han’s (2023) model of EFE provides substantial support for the view that changes in financial literacy do not affect EFE: EFE is directly linked to biopsychosocial developments, not financial information deficits. Directly helping elders build financial literacy, combined with education on basic scamming techniques, would likely have worked 10 years ago, but not in the age of AI. The personalized techniques scammers use with AI are far more prevalent than traditional scams, including phishing emails and telemarketing calls. Emotional capacity is far more active and more stable, given the sole nature of financial literacy in the market, as it cannot keep up with the changing, AI-driven scams.

V. The Regulatory Landscape and Where It Falls Short

1. Too Many Agencies, No Clear Leader

In the US, regulatory action aimed at countering EFE falls dismally short. Despite rising national budgets and debt, the US lacks a regulatory agency to combat EFE. With jurisdiction over the issue spread across both state and federal entities, there is currently no coordinated effort to combat it. A little bit of detail on this distributed effort, FinCEN fulfills the Bank Secrecy Act reporting; the CFPB (Consumer Financial Protection Bureau) regulates financial products in the consumer market; the SEC helps connect BSA elements to financial advisors; the FTC prosecutes fraud, states handle criminal crackdowns, and Adult Protective Services handles social issues that arise. The agencies of the FDIC, OCC, Federal Reserve, NCUA, CFPB, FinCEN, and state entities acknowledged the problematic nature of enforcing a coordinated effort to combat EFE. Still, they failed to establish an enforcement policy to coordinate such a collective effort (Interagency Statement on EFE, 2024). This shows the inherent weakness of the current system in coordinating efforts to counter EFE and demonstrates that a central effort is necessary to combat the issue fully.

Figure 3

Matrix of Interagency Regulatory Fragmentation in United States EFE Oversight

I. Financial Flows and Transaction Monitoring

   A. Financial Crimes Enforcement Network (Suspicious Activity Report processing)

   B. Securities and Exchange Commission (Oversight of registered financial advisors)

   C. Federal Deposit Insurance Corporation & OCC (Commercial banking regulation)

II. Market Conduct and Consumer Protection

   A. Federal Trade Commission (Data tracking and consumer fraud prosecution)

   B. Consumer Financial Protection Bureau (Retail financial product oversight)

   C. State Attorneys General (State-level civil litigation and enforcement)

III. Social Infrastructure and Physical Welfare

   A. Adult Protective Services (Social work interventions and welfare checks)

   B. State and Local Law Enforcement (Criminal investigations and local prosecution)

IV. Structural Consequence

   The fragmentation of oversight across these discrete domains results in isolated 

   data siloes, overlapping jurisdictional boundaries, and the absence of a 

   centralized federal enforcement authority.

2. The BSA’s Blind Spot

Under the BSA (Bank Secrecy Act), the Suspicious Activity Report (SAR) system serves as the primary systemic protection against EFE. However, a significant problem is that this system was not engineered to address the problem it is now tasked with. SAR serves to identify unusual transactions within accounts, such as large-sum wire transfers to new entities. This method is effective for identifying outbound scamming activities. For trusted relationship transactions that are exploitive, this approach fails. For example, a daughter of an older adult transfers funds out of her mother's account. This looks fine on paper, and that is where the SAR's deficiency lies. FinCEN (2024a) reported that 80% of EFE BSA filings involve outbound scamming efforts, while only 20% involve trusted relationship scams. With a smaller percentage, the SAR efforts neglect this classification of scams, and therefore are unable to serve 20% of victims. This truly reflects the limitations of current systems in preventing and limiting the impacts of EFE across all cases. 

3. The Financial Exploitation Prevention Act

In the wake of the uncoordinated regulatory response from state and federal governments, the most effective legislative response to EFE is the Financial Exploitation Prevention Act (H.R. 2478 and S. 2840). This piece of legislation would allow financial institutions to redeem funds lost by elder consumers to EFE and halt financial transactions deemed unusual. As of 2026, the Senate version of this legislation is still in the works, with the House having passed the bill in 2024. Even with this legislative delay in the federal government, the bill's concept is promising: prevent the negative financial effects of EFE by reimbursing seniors and preventing funds from leaving the domestic economy.

However, the bill's legalities are still being hashed out, for good reason. First off, the bill interferes with the right of consenting adults to have full control over their finances, which contradicts fundamental legal precedent. Also, it lacks sufficient clarity on what “suspicious activity” means, with no real way to standardize identification. 

4. The International Problem

 A large share of the damage caused by EFE stems from international organizations and individuals. Many of the major scamming methods, including pig-butchering, crypto investment scams, and tech support pop-ups, are carried out using outsourced labor. The root of this international problem is that US authorities lack jurisdiction to investigate and prosecute these crimes. Focusing on the major crypto issue, the Financial Action Task Force (FATF), the international organization on money laundering, has only 40 countries in compliance. Laundering standards leave many exchanges and cryptocurrencies without proper monitoring and regulation by authorities, despite extensive domestic regulation in the US. Therefore, to address this international issue with EFE, international cooperation and treaties are needed to halt its international sources.

VI. Key Governance Tensions and the Path Forward

1. Five Tensions That Must Be Resolved

Building on the earlier optimistic sentiment about resolving the EFE issue, the organization needs to address the 5 core issues that underlie the broader concept. 

Autonomy vs. Protection- One of the major issues with combating EFE is tackling the issue of whether to interfere with the freedom of choice for senior citizens to protect them from fraud, or leave them to their own devices in terms of executing decisions. Sustein and Thaler (2008) suggest a middle ground on this issue, offering seniors a protective service, provided by the government or healthcare providers, that they could opt out of if they feel it is necessary. This gives seniors the peace of mind of protection if they choose it, and individual freedom if they feel that there is an intrusion on their personal decision-making. However, there is still opposition from disability advocates claiming that a default protection system, just for senior citizens, is indirectly classifying them as incompetent, which could, in turn, create systemic prejudice. 

Privacy vs surveillance- With fraud, especially in the AI era, it typically goes under the radar until after the financial harm has already been done, when it is uncovered. This leads authorities to want to detect fraud before it occurs, which would require monitoring elders’ financial behavior and tracking their transactions. Though this might seem like a feasible way to stop fraud before it happens, this raises major privacy concerns. There is the concern that the wrong people could gain access to the elder’s data. Who is responsible and in charge of monitoring accounts that freeze due to suspicious activity? These questions, given the ambiguity of the issue in regulatory policy, have no clear, accessible answers. After the Supreme Court’s decision in Carpenter v. United States (2018), which ruled that the 4th Amendment is violated when government entities access people’s cellphone data, the issue is even more complex.

Compliance Costs vs. Financial Inclusion - The cost of compliance, including government regulations that require banks to complete and report on anti-money laundering and fraud-prevention activities, which in turn raise expenses for banks when servicing senior citizens. If compliance costs continue to grow, banks may reduce services for senior citizens, which could, in turn, force seniors to store their money in prepaid cards, wire services, and the aforementioned crypto market. Therefore, more regulation could, counterintuitively, leave seniors more exposed, worsening the issue.

AI governance as a double-edged tool- With the counterintuitive logic of compliance regulation creating harm for seniors instead of helping them, similar reasoning can be applied to the issue of AI governance. As AI restrictions continue to be enforced and strengthened, this can inhibit defense mechanisms more than they do offensive systems from combating online fraud. On the other hand, if applied too loosely, there would be a lack of regulation protection against AI-powered scamming activity, leaving seniors vulnerable to EFE, yet again, a poor outcome. Taking these two opposite enforcement levels into account, as in the compliance issue, there needs to be a compromise between heavy and light enforcement of AI governance, with a clear link between regulatory policy and AI being crucial for the success of reducing EFE.

Measurement and evidence- With $10 billion in confirmed losses and $137 billion in approximate true losses, the disparity between the estimate and the “confirmed” quantity shows an issue with policy and its implementation. Effective regulation yields accurate data. Linked back to the decentralized enforcement of the issue among state and federal entities, the collection and organization of data for EFE is uncoordinated and disorganized, relying on public reporting. This disparity in the data, and the fact that self-reporting is extremely common, reform the data analysis process on EFE. Utilizing FTC data capabilities, FinCEN filings, healthcare data, and credit bureau data, a more accurate measurement of the amount of EFE occurring, and therefore better combat the issue.

2. What Needs to Happen Next

Settling these issues requires more effective action than advocacy campaigns and reporting requirements. It requires a change in the regulatory landscape for handling EFE to aid seniors for the greater good of the public, not just an issue that affects seniors themselves. This kind of issue needs federal investment and institutional change, not just uncoordinated action amongst states or local counties.

Through economic and public policy research, several priorities in implementing this change are clear. Economists need to contribute to the issue by modeling the fiscal effects of EFE, placing it within the scope of the aggregate market rather than treating it as merely an economic issue for seniors. On the other side of the issue, public policy researchers need to examine policy at both the federal and state levels and, from an objective standpoint, evaluate what is most effective in preventing EFE without the rise in compliance costs currently occurring. Moreover, building on the relationship between the federal and state governments on this issue, regulators and government officials need to determine whether EFE falls under federal or state jurisdiction, and who should have clear authority to enforce regulatory policy. This would help address the serious issue of conflicting authority between local and federal entities in regulating EFE-related issues.

VII. Conclusion

The issue of EFE is not a small-scale matter revolving around the cognitive capacity of seniors and their ability to resist scams individually. It is a systemic market and regulatory failure, in which a generation of older adults is being suffocated by the rise of generative AI, a lack of effective regulatory efforts designed to handle rapid technological change, and an uncoordinated response effort split between state and federal entities. 

The research into the biology of the issue yields an explanation of the individual harm caused by EFE; the economic research shows the broad economic losses of EFE; and the regulatory analysis shows that existing policy and regulatory infrastructure needs drastic reform to combat the issue in the evolving digital age. Synthesizing these perspectives, it is clear that EFE is a systemic issue, not an individual one that can be fixed through financial literacy.

The conflicts in efforts to remedy this issue, which involve autonomy and protection, privacy and surveillance, compliance and inclusion, AI offense and defense, and measurement and policy, are in desperate need of reform and revision at both the national and international levels. They are not obstacles that authorities should avoid and let fester, as they have done in the past. In light of the aforementioned change that is needed, regulators, government officials, economists, and public policy experts can soon collaborate to help seniors secure the financial security and, hopefully, the independence they are entitled to in the ever-changing digital age.

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