Category: Finance

Artificial intelligence (AI) is rapidly revolutionizing our lives, interactions, and finances. It offers a transformative opportunity to boost financial inclusion, especially in emerging and developing economies (EMDEs). AI enables financial institutions to operate more efficiently and cost-effectively, improving performance and unlocking capabilities beyond human limits, particularly in terms of speed and precision. For example, AI-powered digital credit now serves millions of consumers with data trails who previously lacked access to formal credit. AI can also streamline customer onboarding, personalize financial products, and assist in daily financial management. 

We have started exploring how AI exacerbates risks for consumers and how it can help different ecosystem actors, especially authorities and providers, manage those same risks.

But AI’s application in finance is also accelerating and exacerbating a range of risks for the financial sector, including risks for consumers who increasingly use digital financial services (DFS). If not properly addressed, these risks could undermine the progress AI aims to achieve and derail efforts to build more Responsible Digital Finance Ecosystems. As part of CGAP’s agenda to help digital finance become more responsible, we have started exploring how AI exacerbates risks for consumers and how it can help different ecosystem actors, especially authorities and providers, manage those same risks.

Using CGAP’s DFS consumer risks typology established in 2022, we found that AI can exacerbate four main types of risks. 

1) Fraud

AI is making fraudsters more “intelligent” by personalizing services. Generative AI tools like Large Language Models (LLMs) are used for sophisticated phishing scams and identity theft. The ease of creating deepfakes, cracking passwords, and manipulating text poses a significant threat. No-code AI tools lower the barrier for cybercriminals, enabling easier creation of malware and automated attacks. At a recent FinCoNet conference last November, several cases of central bank officials being impersonated using deep fakes were discussed, including the Central Bank Governor of Romania. 

2) Consumer data misuse

The rapid deployment of LLMs introduces poorly understood risks, including system prompts containing sensitive data. Data misuse is exacerbated by the lack of transparency in many AI systems. While AI can unlock financial services for the unbanked, algorithms trained on biased data or data that does not represent traditionally excluded people can amplify societal biases. In the UK, the head of the Financial Conduct Authority has warned that the use of AI in the insurance sector could lead some vulnerable consumers to become uninsurable. This raises critical questions for inclusive finance. 

3) Lack of transparency

The “black box” nature of many AI models makes it difficult to identify and address these biases, leading to potentially harmful consequences for vulnerable consumers. For example, some unscrupulous financial service providers can exploit consumer vulnerability to charge an unfair price by assessing consumer transactions and behavior to see if they may have an urgent need to borrow, even at a very high price.

The complexity of AI algorithms may exacerbate the lack of transparency in the financial sector, as it can be difficult to identify and challenge unfair or discriminatory outcomes. As highlighted by the OECD, AI has the potential to enhance large-scale consumer misinformation and disinformation. This risk may lead to reduced trust in the financial sector among consumers and threaten progress in financial inclusion. 

4) Inadequate redress mechanisms 

The shift to AI-driven customer service, while potentially improving efficiency, can also hinder access to redress. Poorly designed chatbots may fail to adequately address complex complaints, leaving consumers frustrated and without effective recourse. They can also give the wrong or incomplete answers to consumers. As stated by CFPB, “Even when chatbots can identify that a dispute is being made by the customer, there may be technical limitations to their ability to research and resolve that dispute.” Social norms around complaints, particularly among vulnerable consumers, can further exacerbate this issue.

We believe that over-indebtedness could be provoked by a combination of the above risks. AI can increase the risks of over-indebtedness, thus negatively affecting financial health. For example, both GSMA and CGAP found some concerning digital credit-related debt stress in West Africa. 

Thankfully, AI also brings new solutions to build more Responsible Digital Finance Ecosystems. If consistently used by consumers, financial service providers, and authorities, the following solutions could result in DFS risk reduction and better outcomes for consumers. 

AI can help consumers improve their digital and financial literacy 

This includes education on fraud prevention, data privacy, and how to recognize and report discriminatory practices. This could be a game changer for vulnerable consumers, such as people with disabilities, who represent about 16% of the global population. As noted in a UN report, “AI makes communication possible through eye-tracking and voice-recognition software, enabling persons with disabilities to access information and education. There is also great potential for AI to help consumers make more savvy decisions and change their daily financial management, thus improving their financial health. 

AI brings new solutions to financial service providers for fraud prevention

Our desk research, conducted with initial inputs from Caribou Digital, found that many fintech firms offer AI-powered solutions to financial service providers to better protect themselves and their consumers from financial fraud. This includes real-time user behavior analysis, fraudulent document detection, SIM swap identification, anti-phishing measures, and proactive warnings to users. Global research found that fraud detection was the most common use of AI in the financial industry. Additionally, AI can play a significant role in helping financial service providers and consumers avoid social engineering. For example, liveness detection AI tools can help avoid identity theft and synthetic IDs. AI-driven nudges could also help providers better train their employees and agents to protect consumers against fraud and other risks.

AI can enable financial service providers to responsibly use consumer data 

This allows FSPs to personalize financial services, with improved customer relationship management (CRM). It can also help them assess consumer needs and offer solutions that correspond to their aspirations and capabilities. For example, AI could help providers identify consumers who are at risk of over-indebtedness. AI chatbots, coupled with human intervention, could ensure a smooth financial journey for consumers. Providers can also use AI to better assess consumer risk profiles and ensure the right outcomes for them. Some digital credit providers use mobile phone data to help identify and segment their customers’ risk profiles. This model can work well if the sector is well-regulated and supervised.

AI-powered suptech can greatly support financial sector authorities

Indeed, several standard-setting bodies, such as the BIS, IAIS, and IOSCO, are already documenting the opportunity for AI to identify and mitigate risks, particularly in the area of anti-money laundering / countering the financing of terrorism (AML/CFT), and fraud detection. Our research conducted in India in collaboration with the Reserve Bank of India Innovation Hub and Decodis showed that LLM could help authorities better understand the nature of risks being faced by digital credit borrowers. Risks included debt collection practices and misuse of consumers’ data. There is also a well-documented case of an AI-powered chatbot used by the BSP in the Philippines that interacts in real time with consumers.

AI holds immense potential for driving financial inclusion in EMDEs, but the jury is still out on whether it can make digital finance ecosystems more responsible. 

AI holds immense potential for driving financial inclusion in EMDEs, but the jury is still out on whether it can make digital finance ecosystems more responsible. In the next few months, we will deepen our understanding of AI-enabled solutions available for key actors in the ecosystem, especially authorities and providers, to curb the rising risks that AI creates for consumers using digital finance. Identifying and implementing these solutions will require an ecosystemic approach with increased collaboration between fintechs, DFS providers, authorities, and consumer representatives, as not one single actor has the means to fully mitigate AI-related risks.

Imagine a world where a single click approves a loan, insurance claims are settled in seconds, and financial advisors aren’t human, but highly intelligent machines. This is not science fiction anymore; it is the reality of today’s financial landscape. As AI continues to revolutionize the financial sector, it brings both unprecedented opportunities and unique challenges. How can public authorities support the financial sector to harness the power of AI while ensuring it operates within safe and ethical boundaries? Following our recent blog on the global AI regulatory landscape, we propose three key considerations that could enable regulatory authorities to proactively support the responsible use of AI in finance: increased coordination across broader policy domains, iterative engagement with multiple stakeholders, and enhanced adaptive regulation. We think this comprehensive approach could help mitigate AI risks and unleash its potential for financial inclusion.

Balancing opportunities and risks 

AI is being used across the financial sector to analyze vast amounts of data about consumers and determine what products they qualify for. These sophisticated tools are used for customer onboarding, credit scoring, insurance underwriting, and claim processing, virtual help desks, robo-advice, trading, portfolio and risk management, fraud detection, cybersecurity, and Anti-Money Laundering/Counter Financing of Terrorism (AML/CFT) compliance. For financial service providers, using AI can unleash efficiency gains and economies of scale. For consumers, it can help deliver tailored experiences and hyper-personalized financial products. However, AI has the potential to amplify existing financial and non-financial risks, compromise market integrity, and cause consumer harm. 

The benefits and risks of AI largely depend on its use case. We identified three elements to consider in a structured assessment of the benefits and risks of using AI in the financial sector. These include the input data, the model itself, and outputs generated by it. For each of these elements, there are associated opportunities and risks. While we do not intend to present an exhaustive list, Figure 1 summarizes our analysis.

One risk often highlighted when assessing AI is algorithmic biases. This issue can arise from many sources, including the fact that input data can be unrepresentative, incorrect, or incomplete for training the model, and reflect historical biases. Such biases can be embedded during the modelling stage when protected characteristics, such as race, gender, or religion, can be included or retrieved using proxies (e.g., zip code). This can foster output biases, preventing low-income and excluded individuals and businesses from accessing affordable financial products. This is one of the key issues that needs to be carefully addressed so that AI doesn’t create new inequalities or exacerbate existing inequalities that hinder financial inclusion. 

Considerations for public authorities

While the use of AI comes with risks, it is important to consider that in many cases, those are the amplification of existing risks, which may already be covered by existing rules. Even so, specific guidance on how existing regulation can be applied to the use of AI would be beneficial. We have identified three key areas to be considered by public authorities.   

1) Consider broader policy domains and stakeholders

AI in finance covers more than just financial and securities acts – it includes other policy domains such as data privacy, data protection, consumer protection, competition law, operational resilience, recovery planning, and cybersecurity. To ensure a holistic response, financial authorities can consider:

• Harmonizing definitions and ethical principles. A global AI taxonomy could align terminology and frameworks, reducing regulatory arbitrage. Authorities could provide guidance on the implications of adopting ethical benchmarks (e.g., safe, fair, ethical, trustworthy) for input data, algorithm training, and output data.
• Promoting cross-sectoral and multi-stakeholder coordination. Authorities could foster knowledge exchange of use cases with financial firms, technology providers, regulators, government, and civil society.
• Incentivizing data-sharing schemes. Data fuels AI. Authorities could strengthen data infrastructure, promote data-sharing, and establish governance structures for data-sharing through open data frameworks that can encourage AI innovation, as well as put in place necessary data protection mechanisms.
• Ensuring data protection compliance. This would include empowering customers to have more control over their data and enforcing the right to “be forgotten” when input data is no longer needed.
• Assessing AI under competition law. Authorities could evaluate whether AI algorithms could lead to tacit collusive pricing practices that violate antitrust laws or foster unhealthy competition.
• Encouraging the application of existing consumer protection rules. AI can power sophisticated forms of financial fraud, cyber threats, and data privacy concerns, which makes it critical for authorities to ensure consistent treatment under the law for similar products, services, and activities. 

2) Assess the suitability of existing regulation

Even though financial regulation is “technology neutral”, AI brings new challenges and amplifies financial and non-financial risks, due to its complexity, potential for autonomous decision-making, and governance issues. Some areas for authorities to consider when evaluating the suitability and clarity of their existing rules to mitigate AI-related risks include: 

• Financial stability and contagion risks: Identify whether risk management frameworks quantify and mitigate AI-driven market fluctuations, including potential herding or contagion effects, magnifying market booms and busts.
• Third-party and outsourcing risk: Assess the implications of outsourcing of critical infrastructure and processes, including AI tools. Consider the potential concentration of third-party risk (especially the risks associated with relying on a small number of dominant cloud providers) and its implications for systemic and reputational risk whenever unintended data leakages occur.
• Data model risk: Examine whether existing risk management frameworks account for AI specifics such as algorithm biases and model hallucinations.
• Cybersecurity risk: Clarify the responsibility of AI providers, developers, and users in protecting personal data if data breaches or misappropriation occur.
• Explainability and transparency: Update disclosure requirements to help consumers and investors understand AI-generated outcomes (e.g., credit scoring decisions or investment decisions). 

For each of these risks, it is crucial that authorities define clear monitoring metrics and periodically update them to align with the rapidly changing AI landscape, assessing their accuracy and relevance to understand the potential loss and harm. 

3) Conduct targeted consultations to inform regulation

The debate on AI is often informed by hypothetical concerns without adequate evidence. Conducting public consultations to gather industry feedback can offer regulators actionable and up-to-date intelligence. As an example, financial authorities in the EU, Japan, New Zealand, the UK, and the U.S. have conducted public consultations to identify the most pressing AI risks for market participants. In addition, authorities can consider implementing consumer advisory panels to facilitate a nuanced understanding of consumer protection and customer experience issues with AI-powered tools, and ultimately disseminate findings around consumer and conduct risks.  

The financial industry as a whole needs to have a conversation about how AI can be used to foster financial inclusion and create positive outcomes for everyone.

Regulating AI is only one piece of the puzzle. The financial industry as a whole needs to have a conversation about how AI can be used to foster financial inclusion and create positive outcomes for everyone. As the use of AI continues to grow, CGAP is working to better understand how AI can positively impact financial inclusion objectives and how the benefits for traditionally excluded and underserved customers can be materialized while mitigating risks.    

For years, there has been some hope that inclusive credit fintechs—those targeting micro and small enterprises (MSEs) with responsible digital credit—would narrow the financial inclusion gap. The expectation was that they could outperform traditional microfinance institutions (MFIs) by being faster, cheaper, more scalable, and better equipped to serve hard-to-reach clients. While some success stories have emerged, the reality is far more complex.

Inclusive digital credit isn’t just about algorithms; it also relies on human interaction to bridge the gap.

Building a successful credit fintech is not an overnight achievement, and most successful fintechs experience a challenging route to success. Many started in modest settings—small apartments, student dorms, or co-working spaces—bootstrapping their way from concept to minimum viable product (MVP). This often happens with negligible budgets and funding scraped together from savings, family, and friends. Most fintech initiatives never even make it beyond this early stage. The reason is simple: developing a robust and responsible digital credit platform takes time and significant capital. It involves extensive development work, integration with existing digital infrastructure, rigorous data security and compliance measures, and the continuous refinement of risk assessment models.

The challenge intensifies when fintechs seek to serve excluded and vulnerable populations. Technology alone isn’t enough — trust must be built, often requiring field agents to onboard and support clients. Inclusive digital credit isn’t just about algorithms; it also relies on human interaction to bridge the gap.

Fintech is as much a long-term commitment as it is a disruptive force

Unlike traditional financial service providers (FSPs), which use standardized credit methodologies with clear benchmarks, fintech lending models are often untested and opaque, making it challenging for investors to conduct risk assessments since fintech approaches don’t align with conventional metrics. To evolve into viable, self-sustaining businesses, these credit-granting fintechs need to scale a robust portfolio, which requires financing. However, because their models are difficult to assess, securing funding from conventional investors is a challenge. They first need to establish a series of successful lending cycles to prove their viability and attract impact investors, but to do that, they need financing. It’s a catch-22.

Financing remains a major hurdle for inclusive fintechs, which, unlike MFIs, lack the equity base to leverage portfolio financing. Traditional debt is out of reach for young, unproven fintechs, leaving equity as the primary option. Early-stage fintechs with thin management teams dedicate most of their time to chasing and pitching to potential funders, rather than focusing on building the business and understanding market realities. The process is time-consuming and often does not yield the desired results. Fintech failures are not exceptions but an industry-wide reality, with most initiatives falling short. Not every idea deserves funding, as many fintechs struggle due to weak leadership, poor risk assessment, or an inability to find the right product-market fit. Given these challenges, it’s understandable that asset managers hesitate to back early-stage inclusive fintechs.

However, a handful of forward-thinking asset managers are rewriting the investment playbook

 Through establishing data integrations with early-stage fintechs, these asset managers can adopt more sophisticated risk evaluation methods. Access to real-time portfolio data enables them to continuously assess performance and adjust their risk strategies. This approach allows them to enter with debt investments at an early and even pre-profit stage, measure performance over time, and release future installments based on the fintech’s evolving needs and risk profile. 

A good example of the potential impact of data-driven asset managers can be seen in ALMA Sustainable Finance’s investment in Monedo, an Indian NFBC offering loans and other financial services to underserved MSMEs and consumers, using its digital platform. In 2022, ALMA issued a USD 4.5 million senior loan to Monedo, whilst the fintech was still loss-making. Although Monedo had received some small ticket loans (around USD 100 thousand) from three Indian investors, ALMA’s entry was their first significant institutional investment.

ALMA’s investment was crucial in enabling Monedo to scale rapidly and achieve profitability ahead of schedule. Following ALMA’s senior debt, disbursements surged by 516% in just 12 months, allowing Monedo to break even within 24 months—12 months ahead of the initial forecast. Additionally, ALMA’s backing boosted Monedo’s market credibility, attracting larger institutional investors and securing a follow-on funding round, positioning Monedo for national expansion, cementing its role as a key player in financial inclusion.

Another strong example of the impact of innovative asset managers is the partnership between Watu, an African mobility financing company, and Lendable, a data-driven investor. As Watu’s first institutional debt investor, Lendable made a USD 350,000 debt investment in August 2017 and integrated with the company’s data systems to analyze the performance, cash flows, and margins of Watu’s moto taxi financing model. This data-driven approach provided valuable insight into Watu’s financial sustainability and growth potential, paving the way for larger commitments over time.

Lendable has been instrumental in Watu’s expansion into new markets. In 2020, it invested in Watu Uganda, which reached breakeven by 2021. With Lendable’s backing, Watu’s Ugandan loan book grew from USD 5 million in 2020 to USD 83 million by 2024. Lendable has since expanded its debt facilities to support Watu’s entry into Tanzania. Additionally, it has facilitated  USD 18.6 million in syndications from co-investors, including leading development finance institutions (DFIs). Through its Maestro data platform, Lendable provides real-time, granular visibility into and verification of Watu’s transactions, enhancing transparency and trust in the collateral base.

For these innovative asset managers, these partnerships offer the opportunity to achieve healthy and even above-market returns by identifying and supporting high-potential fintechs that traditional investors will not yet serve, demonstrating both financial acumen and a commitment to inclusive finance. As such, data-driven asset managers are not competing with traditional investors; rather, they are building the pipeline for them. By scaling fintechs to a stage where conventional asset managers feel comfortable engaging, they add a unique and critical layer to the financial ecosystem. In doing so, they are not just funding innovation—they have the potential to reshape the trajectory of inclusive finance, accelerating the development of sustainable fintech models, and ultimately upgrading the entire investment landscape.

For a deeper dive into the inclusive credit fintech financing gap and the innovative ways data-driven asset managers are stepping in, check out CGAP’s recently published research: Innovative Financing for Inclusive Credit Fintechs in Africa.

The Bureau of Economic Analysis released the latest U.S. gross domestic product data on April 30. In the first three months of 2025, it said, GDP contracted by 0.3%. The GDP growth rate captures the pace at which the total value of goods and services grows or shrinks. Together with unemployment and inflation, it usually receives a lot of attention as an indicator of economic performance.

Some economists and analysts said the economy might not be as bad as this rate’s decline might suggest. While this is the first time in three years that GDP has shrunk instead of growing, it is a relatively small decline.

This raises a critical question: Does a relatively small GDP contraction mean the economy is in trouble? I have spent much of my working life studying economic well-being at the level of individuals or families.

What I’ve learned can offer a different lens on the economy than you’d get from just focusing on the most popular indicators, such as the GDP growth rate.

GDP problems

The GDP growth rate has many limitations as an economic indicator. It captures only a very narrow slice of economic activity: goods and services. It pays no attention to what is produced, how it is produced or how people assess their economic lives.

GDP gets a lot of attention, in part, because of the misconception that economics only has to do with market transactions, money and wealth. But economics is also about people and their livelihoods.

Many economists would agree that economics treats wealth or the production of goods and services as means to improve human lives.

Since the 1990s, a number of international commissions and research projects have come up with ways to go beyond GDP. In 2008, the French government asked two Nobel Prize winners, Joseph Stiglitz and Amartya Sen, as well as the late economist Jean-Paul Fitoussi, to put together an international commission of experts to come up with new ways to measure economic performance and progress. In their 2010 report, they argued that there is a need to “shift emphasis from measuring economic production to measuring people’s well-being.”

Considering complementary metrics

One approach is to use a composite index that combines data on a variety of aspects of a country’s well-being into a single statistic. That one number could unfold into a detailed picture of the situation of a country if you zoom into each underlying indicator, by demographic group or region.

The production of such composite indices has flourished. For example, the Human Development Index of the United Nations, started in 1990, covers income per capita, life expectancy at birth and education. This index shows how focusing on GDP alone can mislead the public about a country’s economic performance.

In 2024, the U.S. ranked fifth in the world in terms of GDP per capita, but was in 20th place on the Human Development Index due to relatively lower life expectancy and years of schooling compared to other countries at the top of the list, like Switzerland and Norway.

Monitoring other indicators

Another approach is to rely on a larger number of indicators that are frequently updated. These other data points reflect a variety of perspectives about the economy, including subjective ones that convey personal perceptions and experiences.

For instance, in addition to inflation rates, there is data on stress due to inflation as well as inflation expectations. Both offer insights into people’s perceptions, perspectives and experiences about inflation.

During the COVID-19 pandemic, the annual U.S. inflation rate increased from 1% in July 2020 to 8.5% in July 2022. My research partners and I found, using U.S. Census data, that more than 3 in 4 adults in the U.S. were experiencing moderate or high levels of stress due to inflation at that time and continued to do so even after inflation went down in 2023.

More recently, the Trump administration’s sporadic tariff changes have made future prices more uncertain, which exposes people to risks. That, in turn, makes people adjust their expectations and feel worse off.

The share of consumers expecting higher inflation rates has climbed sharply in 2025, while consumer confidence has declined abruptly. About 1 in 3 consumers expect that there will be fewer jobs created in the next six months, which is almost as low as during the Great Recession of 2007-2009.

Consumers also have negative expectations about their own future income and worry about their own economic status.

At this moment, the U.S. economy has not officially entered a recession – which requires a longer period of GDP contraction than just one quarter. Although unemployment and inflation rates remain relatively low, the broad picture of the economy that takes into account people’s expectations and perceptions is troubling. To be clear, I’m not saying that just because of what the GDP data may indicate.

This article includes material from an article originally published on Aug. 7, 2018.

Whistleblowers – people who expose wrongdoing within their organizations – play a crucial role in holding governments and corporations accountable. But speaking up can come at a cost. People who report misconduct often face retaliation, job loss or legal threats, making whistleblowing risky and challenging. And when legal protections for whistleblowers are weakened, the risks only grow.

That’s exactly the situation many workers face today.

In the U.S., a Trump administration executive order threatens to effectively strip thousands of federal workers’ rights to whistleblower protection. The executive order is part of a larger effort to reclassify civil servants as “at-will” workers who can be sacked at any time for any reason. While federal workers have enjoyed protection against whistleblower reprisal for decades, those safeguards are now under threat. And this comes as private-sector whistleblowers have increasingly faced reprisal, too.

Yet while the risks are real, whistleblowing isn’t impossible. Indeed, after researching whistleblowing for over 10 years, I’ve observed that insiders who successfully sound the alarm often do so with help − by partnering with allies who can amplify their message and help shield them from retaliation.

Meet the ‘regulators of last resort’

My new book, “Regulators of Last Resort: Whistleblowers, the Limits of the Law and the Power of Partnerships,” tells the stories of whistleblowers from Facebook, Amazon, Theranos, U.S. Immigration and Customs Enforcement detention centers and Ireland’s public electricity service. In each case, the worker suffered reprisal and was aggressively silenced. In each case, they persisted, and allies emerged to help.

For Facebook employee Frances Haugen, finding an ally meant teaming up with Wall Street Journal reporter Jeff Horwitz, a specialist in tech who had been writing about Facebook’s misdeeds for some time. When Haugen decided to go public about the social media platform’s knowing exploitation of teenagers and its awareness of the violence incited by poorly regulated non-English versions of its site, Horwitz was pivotal in orchestrating when and how the newspaper articles would appear, helping maximize their impact and granting Haugen control over how her story was told.

This partnership was no accident; Haugen chose the reporter and tech expert carefully. “I auditioned Jeff for a while,” she later told a reporter. “One of the reasons I went with him is that he was less sensationalistic than other choices I could have made.”

Indeed, many whistleblowers disclose with the wrong journalist, leaving themselves open to attack.

At Theranos – a multibillion-dollar biotech company that turned out to be a fraud – a lawyer “friend of a friend” gave whistleblower Erika Cheung critical advice about disclosing to a regulator. This was a lifeline for the recent graduate, who feared for her career and safety after being threatened by bosses and lawyers and warned to stay silent and obey her nondisclosure agreement. Meanwhile, Cheung had no money for formal legal representation. It was that call to the lawyer that made all the difference, Cheung told me. “He said, ‘You can whistleblow.’”

Her contact explained that if she disclosed to the Centers for Medicare & Medicaid Services, she could avail of whistleblower protection and break her NDA. She would have to do it right and focus on the details: to highlight Theranos’ “regulatory noncompliance” and demonstrate the firm was violating the rules for proficiency testing. But all it would require of Cheung was a simple email to the right organization.

Finally, my research also detailed the many colleagues at Amazon who supported whistleblowing manager Chris Smalls in disclosing risks to life and health during the early days of the COVID-19 pandemic in New York. When Smalls was fired for speaking out and subject to racist language in internal memos about the incident that were later leaked, his close colleague Derrick Palmer described his response. “I was appalled,” Palmer said. “I just knew that they wanted to – pretty much – silence the whole effort. Anyone speaking out. That was how they were going to treat them, moving forward. Including myself.”

This strengthened Palmer’s determination to help Smalls. Meanwhile, the leaked memo prompted letters of support and emails “from people from all over the country – Amazon workers, non-Amazon workers, that just want to help advocate as well,” as Smalls put it. In the days and weeks after, workers held demonstrations at Amazon facilities all across the U.S., with banners declaring solidarity with the New York warehouse whistleblowers.

No whistleblower is an island

These allies often go overlooked when the media focuses on whistleblowers. But their support is critical, particularly in an era when protections for workers who speak up are coming under increasing threat worldwide.

Organizing whistleblowing allies involves strategy, and some nonprofit and civil society groups have become experts in this domain. Leading the way is the U.S. Government Accountability Project and its “information matchmaking” approach. The idea is simple: Whistleblowers need a whole team of other people – from experts to members of the public – on their side. And this takes planning.

For years, lawyer-activists like those at the Government Accountability Project have been treating whistleblower protection and support efforts as holistic campaigns that entail a media operation and networking effort, as well as a legal defense.

Take the example of Dawn Wooten, a former nurse at the Irwin County Detention Center – a U.S. Immigration and Customs Enforcement contractor – who encountered and disclosed medical misconduct and critical failures. Dana Gold at the Government Accountability Project supported her whistleblowing with other activists, enlisted civil society groups and politicians in the cause, helped land newspaper articles in The Guardian and The New York Times, and even arranged a New Yorker podcast in which Wooten told her story.

The information went viral, and multiple investigations ensued. Within a year, the Department of Homeland Security directed ICE to formally end its contract with the Irwin County Detention Center, citing the revelations made public by Wooten and some of the detained women.

None of this is straightforward. In most whistleblowing disputes, the organization holds the balance of power. It has the files, the witnesses and the money to pay good lawyers. I’ve found that whistleblower allies must work with whatever limited resources they can marshal to give themselves an advantage. This means engaging influential people who might help, including pro bono lawyers, specialists who can give evidence, concerned regulators and beat journalists. In short, what is necessary is experts across all domains who are interested in the story and willing to help. And it’s the collective effort that matters.

Even with this support, however, whistleblowers don’t have it easy. In many high-profile cases where a disclosure is made public and a whistleblower is clearly vindicated and recognized as a courageous truth-teller, they can suffer afterward. Potential employers can balk at the prospect of hiring a whistleblower, even a celebrated one. And vindictive organizations can and do continue retaliating, even years after a story has dropped off the front pages.

Whistleblower allies and their strategies don’t offer a magic bullet. But they can help tip the balance of power, bringing public opinion to bear on an employer bent on reprisal or a government intent on coddling the powerful.

It’s telling that U.S. President Donald Trump’s administration wants to fire bureaucrats. In its view, bureaucrats stand for everything that’s wrong with the United States: overregulation, inefficiency and even the nation’s deficit, since they draw salaries from taxpayers.

But bureaucrats have historically stood for something else entirely. As the sociologist Max Weber argued in his 1921 classic “Economy and Society,” bureaucrats represent a set of critical ideals: upholding expert knowledge, promoting equal treatment and serving others. While they may not live up to those ideals everywhere and every day, the description does ring largely true in democratic societies.

I know this firsthand, because as a sociologist of work I’ve studied federal, state and local bureaucrats for more than two decades. I’ve watched them oversee the handling of human remains, screen travelers for security threats as well as promote primary and secondary education. And over and over again, I’ve seen bureaucrats stand for Weber’s ideals while conducting their often-hidden work.

Bureaucrats as experts and equalizers

Weber defined bureaucrats as people who work within systems governed by rules and procedures aimed at rational action. He emphasized bureaucrats’ reliance on expert training, noting: “The choice is only that between ‘bureaucratisation’ and ‘dilettantism.’” The choice between a bureaucrat and a dilettante to run an army − in his days, like in ours − seems like an obvious one. Weber saw that bureaucrats’ strength lies in their mastery of specialized knowledge.

I couldn’t agree more. When I studied the procurement of whole body donations for medical research, for example, the state bureaucrats I spoke with were among the most knowledgeable professionals I encountered. Whether directors of anatomical services or chief medical examiners, they knew precisely how to properly secure, handle and transfer human cadavers so physicians could get trained. I felt greatly reassured that they were overseeing the donated bodies of loved ones.

Weber also described bureaucrats as people who don’t make decisions based on favors. In other forms of rule, he noted, “the ruler is free to grant or withhold clemency” based on “personal preference,” but in bureaucracies, decisions are reached impersonally. By “impersonal,” Weber meant “without hatred or passion” and without “love and enthusiasm.” Put otherwise, the bureaucrats fulfill their work without regard to the person: “Everyone is treated with formal equality.”

The federal Transportation Security Administration officers who perform their duties to ensure that we all travel safely epitomize this ideal. While interviewing and observing them, I felt grateful to see them not speculate about loving or hating anyone but treating all travelers as potential threats. The standard operating procedures they followed often proved tedious, but they were applied across the board. Doing any favors here would create immense security risks, as the recent Netflix action film “Carry-On” − about an officer blackmailed into allowing a terrorist to board a plane − illustrates.

Advancing the public’s interests

Finally, Weber highlighted bureaucrats’ commitment to serving the public. He stressed their tendency to act “in the interests of the welfare of those subjects over whom they rule.” Bureaucrats’ expertise and adherence to impersonal rules are meant to advance the common interest: for young and old, rural and urban dwellers alike, and many more.

The state Department of Elementary and Secondary Education staff that I partnered with for years at the Massachusetts Commission on LGBTQ Youth exemplified this ethic. They always impressed me by the huge sense of responsibility they felt toward all state residents. Even when local resources varied, they worked to ensure that all young people in the state − regardless of sexual orientation or gender identity − could thrive. Based on my personal experience, while they didn’t always get everything right, they were consistently committed to serving others.

Today, bureaucrats are often framed by the administration and its supporters as the root of all problems. Yet if Weber’s insights and my observations are any guide, bureaucrats are also the safeguards that stand between the public and dilettantism, favoritism and selfishness. The overwhelming majority of bureaucrats whom I have studied and worked with deeply care about upholding expertise, treating everyone equally and ensuring the welfare of all.

Yes, bureaucrats can slow things down and seem inefficient or costly at times. Sure, they can also be co-opted by totalitarian regimes and end up complicit in unimaginable tragedies. But with the right accountability mechanisms, democratic control and sufficient resources for them to perform their tasks, bureaucrats typically uphold critical ideals.

In an era of growing hostility, it’s key to remember what bureaucrats have long stood for − and, let’s hope, still do.