Malintegrity and the Group Dynamics of Corruption: How Collective Silence and Complicity Drive the Hidden Majority of Dishonest Acts

Author: Bharat Bhushan (Bharat Luthra)

Abstract

This paper argues that corruption is not merely an individual act of moral failing, but often a group-based structural phenomenon, which we term malintegrity. Malintegrity occurs when a group (inside an organization, institution, or society) sustains a false appearance of integrity while in fact engaging in collective complicity, silence, or active wrongdoing. Through review of empirical studies, network analyses, whistle-blowing research, agent-based modelling of corruption contagion, and extensive case studies from India, this paper shows how group structures, peer pressure, suppression of whistle-blowers and collective rationalisation amplify corruption far beyond isolated acts. We also examine data trends (globally and for India), draw on public data, and develop fifteen detailed Indian case-studies to demonstrate the arguments. The paper concludes with policy implications: targeting group-level mechanisms, strengthening whistle-blower protection, redesigning organisational structures and applying network analysis to corruption networks can help break the malintegrity cycle.

1. Introduction

Corruption is typically framed as individual acts of bribery, embezzlement, favouritism, or abuse of power. However, many recent studies show corruption often flourishes within small groups, networks or collective settings—where individuals fail to act alone, and where silence, complicity or group pressure enable wrongdoing. This phenomenon we call malintegrity: the corrupted state of integrity occurring via group corruption, where individuals who could act as whistle-blowers instead join, enable or permit corruption.

The purpose of this paper is to synthesise evidence supporting the claim that malintegrity is a major driver of corruption globally: that group dynamics and collective silence are foundational, often more so than isolated moral lapses. By integrating data trends, public datasets, modelling work, whistle-blower literature and fifteen Indian case studies, we illustrate how group structures and failure of whistle‐blowing foster systemic corruption. This deeper empirical grounding adds weight to the conceptual argument and highlights how malintegrity enables scale, invisibility and persistence.

2. Conceptualising Malintegrity

Malintegrity is defined as:

The corrupted state of integrity that arises through group corruption, where individuals collectively choose dishonesty, silence or complicity instead of truth and accountability. It represents a false or distorted form of integrity, especially evident when a potential whistle-blower, or one capable of opposing wrongdoing, instead joins or enables corruption.

Key features:

Group orientation: Not just “one bad apple,” but a cluster of actors, often embedded in a network, who sustain corrupt practices.
False appearance of integrity: Outwardly, the group projects legitimacy, norms of honesty, but inwardly is corrupted.
Whistle-blower failure: Individuals who might raise alarm choose silence, are coerced, or are co-opted.
Collective momentum: Through group peer pressure or structural incentives, wrongdoing becomes normalised.

This conceptualisation aligns with recent network and modelling research showing how corruption spreads more rapidly and extensively when peer and group interactions dominate, as opposed to isolated dyadic corruption.

3. Empirical and Modelling Evidence

3.1 Network analyses of corruption scandals

Research into corruption networks has found that scandals often involve small groups or modules rather than lone actors. For example:

A study titled “The dynamical structure of political corruption networks” analysed scandals in Brazil over 27 years and found corruption networks comprised hubs and modules with small groups typically fewer than eight people. (This supports the idea that corruption is inherently social/collective rather than purely individual.)
More broadly, corruption network research emphasises the importance of network structure (hubs, clusters, modules) in facilitating collusion, concealment and rapid spread of corrupt behaviours.

3.2 Agent-based modelling of corruption contagion

In “Modelling the Impact of Organisation Structure and Whistle Blowers on Intra-Organizational Corruption Contagion”, the authors found that for an organisation of ~1,000 people, when whistle-blowers comprised ≈ 5% of the workforce, the spread of corruption could be constrained; when fewer, corruption spread to the majority.
Another study, “Controlling systemic corruption through group size and salary dispersion of public servants”, found that large interacting groups, narrow salary dispersion and weak reward/penalty differences contributed to systemic corruption in public contracts.
These models highlight how group size, structure, peer interactions and presence (or absence) of whistle-blowers alter corruption dynamics — supporting the malintegrity framework.

3.3 Whistle-blower research & culture of silence

The working paper “Corruption, Intimidation, and Whistle-blowing” shows that monitors (i.e., potential whistle-blowers) may fear retaliation from agents and thus remain silent.
Research on whistle-blowing emphasises that weak protection, social stigma, or organisational culture inhibit disclosure and promote silence.
The failure to blow the whistle is central to malintegrity: the group stays corrupt because individual voices are suppressed or co-opted.

3.4 Global data on corruption detection and reporting

While global comparative data on corruption incidence is limited because hidden wrongdoing is by nature hard to measure, anti‐corruption organisations note that many corrupt acts only come to light when whistle-blowers or investigative journalism intervene. Thus the invisibility of wrongdoing until group breakdown occurs is consistent with the malintegrity thesis: large-scale corruption flourishes because of collective complicity and absence of internal challenge.

4. Data Trends

4.1 Global and national indicators

According to Transparency International, India scored 38 on the Corruption Perceptions Index (CPI) in its most recent year (where 0 = highly corrupt, 100 = very clean) and ranked 96 of 180 countries. (Transparency.org)
The “Corruption Index in India” (Trading Economics) shows an average of ~33.79 points from 1995-2024, with a record high of 41 in 2018 and a low of 26.30 in 1996. (Trading Economics)
Empirical research shows that perceived corruption in Indian firms is negatively associated with economic freedom and other confounding factors. (Cambridge University Press & Assessment)
The paper “Corruption and Economic Growth: A Correlation Study for India” finds moderate positive correlation between CPI and foreign direct investment (FDI) but no strong linear correlation between corruption and GDP growth rate in India. (ijpsl.in)

4.2 Public data on case-registrations

According to the Government of India’s open data platform, the year-wise detail of corruption cases registered by the Central Bureau of Investigation (CBI) from 2017 to 2021 is available, illustrating the trend of formal investigations. (Data.gov India)
For example, official data show 15 cases were pending in the Department of Economic Affairs against officers of the Securities and Exchange Board of India (SEBI) and the Security Printing and Minting Corporation of India Limited (SPMCIL) under various charges. (Press Information Bureau)

4.3 Trends in information-technology intervention

Recent work “The complexity of corruption and recent trends in information technology for combating corruption in India” examines how digital-governance, e-procurement and monitoring systems have evolved. (IDEAS/RePEc)
These technology-driven efforts are important because they affect group-level dynamics: group networks of collusion may adapt to digital transparency, or conversely exploit weaknesses.

4.4 Interpretative implications

These data trends indicate:

That public perceptions of corruption in India remain high (low CPI score) and stagnant, implying enduring systemic issues.
That formal investigations (CBI data) provide a partial “tip of the iceberg” view; large amounts of corruption remain hidden.
That reforms (especially digital governance) are underway, but group-structured collusion (malintegrity) may adapt or reorganise rather than being completely eliminated.
Therefore, malintegrity as a group-based phenomenon remains a salient lens for understanding corruption scale, persistence, and invisibility.

5. Public Data

In order to ground the malintegrity thesis in publicly available data, the following sources are relevant:

The Open Government Data (OGD) Platform of India provides “Year‐wise Detail of Corruption Case Registered by the CBI (2017-2021)”. (Data.gov India)
Transparency International’s country profile for India gives the CPI score, trend, and explanation of key corruption issues. (Transparency.org)
The U4 Anti-Corruption Resource Centre provides state-level breakdowns and institutional assessments in India. (Knowledge Hub)
Research databases (for example Dutta 2024 firm-level data) provide micro-level evidence of corruption perceptions and their effects. (Cambridge University Press & Assessment)
These public data enable an empirical underpinning of the group dynamics of corruption: for instance, one can use the CBI registration data to track the emergence of group-based investigations, or use the firm-level data to examine how peer networks within firms respond to corruption perceptions.

6. Case Studies Illustrating Malintegrity (India-specific)

Below are fifteen illustrative case-studies from India. Each demonstrates group dynamics, complicity, silence, peer pressure or structural collusion — i.e., the essence of malintegrity.

Case 1: Adarsh Housing Society scandal (Mumbai, Maharashtra)

In the Adarsh case, a cooperative housing society in Colaba, Mumbai was constructed ostensibly for war-widows and service personnel, but politicians, bureaucrats and army officers colluded in rule-bending for land, zoning and membership to obtain flats at below-market rates. (Wikipedia)
Group aspects: bureaucrats + military officers + politicians + builders formed a network; whistle-blowing and internal oversight failed for many years; outward legitimacy (a war-widows target) masked the collusion.
Malintegrity features: group orientation, false appearance of integrity, suppressed dissent, and collective capture of the project.

Case 2: Haryana Forestry scam (Haryana)

This is a multi-crore fake plantation scam involving senior officials (including IFS officer Sanjiv Chaturvedi as whistle-blower), where government funds were diverted, plantations faked and collusion widespread. (Wikipedia)
Group aspects: forestry officials, political patrons, contractors. The whistle-blower faced suppression.
Malintegrity features: outward forestry programme legitimacy, inward collusion, group silence and complicity.

Case 3: Gurugram Rajiv Gandhi Trust land grab case (Haryana)

In this land-allocation scandal, panchayat land was leased at below-market rates to the Rajiv Gandhi Charitable Trust during the tenure of then Chief Minister Bhupinder Singh Hooda. The case involves bureaucratic, political and private-sector actors. (Wikipedia)
Group aspects: politicians, bureaucrats, contractors forming network; little internal challenge initially.
Malintegrity features: group capture of land allocation, false façade of charitable trust, suppressed challenge.

Case 4: Uttar Pradesh NRHM scam (UP)

This health-programme scam involved equipment procurement and hospital upgrade contracts under the National Rural Health Mission with forged documents, sub-standard work and alleged murder of whistle-blowers. (Wikipedia)
Group aspects: government officials, private contractors, health service agencies colluding.
Malintegrity features: outward health-programme legitimacy, inward collusion, suppression of whistle-blowers.

Case 5: Maharashtra Irrigation Scam

A major example in Maharashtra where irrigation projects were inflated in cost, work was sub-standard, and more than half the funds were alleged to have been pocketed. (Wikipedia)
Group aspects: ministers, contractors, bureaucrats, engineers.
Malintegrity features: group capture of irrigation tendering, false appearance of water-resource development, normalised corruption.

Case 6: Granite scam in Tamil Nadu

In Tamil Nadu, granite quarrying violations and collusion between officials and quarry companies reportedly caused enormous losses to the exchequer (~₹16,000 crore or more). (Wikipedia)
Group aspects: state officials + quarry operators + politicians.
Malintegrity features: externally nominal quarry regulation, internally large-scale collusion, suppressed dissent.

Case 7: Coal levy scam: Chhattisgarh

While more recent and under investigation, this case involves attachment of properties in a coal levy-related corruption in Chhattisgarh, implicating senior officials. (The Times of India)
Group aspects: bureaucrats + politicians + business associates.
Malintegrity features: group network, hidden accumulation of properties, delay in whistle-blowing and exposure.

Case 8: Ramagundam NTPC/BHEL fraud

At the National Thermal Power Corporation (NTPC) plant in Ramagundam, a ₹35 crore fraud involving collusion between officials of Bharat Heavy Electricals Limited (BHEL), NTPC and private firms was uncovered. (The Times of India)
Group aspects: multiple organisations’ officials + private firms.
Malintegrity features: internal collusion, manipulation of records, group silence until external investigation.

Case 9: Modular ICU fraud Nashik/Malegaon

In Maharashtra, a ₹3.37 crore fraud in modular ICU installation at hospitals involved the tender scrutiny committee, forged licences and documents, and officials approving payments before verification. (The Times of India)
Group aspects: hospital officials + committee members + contractors.
Malintegrity features: medical programme façade, collusion, group suppression of dissent.

Case 10: Dholpur municipal graft trap

Municipal officials in Dholpur were caught accepting bribes for cheque release in drainage works; five officials including engineer, cashier and contractor were caught. (The Times of India)
Group aspects: municipal engineering & administrative staff + contractor.
Malintegrity features: outward civic infrastructure legitimacy, inward collusion, system of bribes accepted by group.

Case 11: Kota patwari land‑bribe case

A Revenue official (patwari) in Kota, Rajasthan, accepted a ₹45,000 bribe from a farmer for land-measurement – illustrating low-level but collective tolerance of corruption. (The Times of India)
Group aspects: revenue official + support staff + underlying system of bribes.
Malintegrity features: the normalisation of small-scale corruption, peer silence, official complicity.

Case 12: Settlement fraud Jaisalmer

In Jaisalmer, four settlement officials were dismissed after investigation into fraudulent plot allocations (about ₹5 crore loss) involving fake plot-books and 216 illegal allocations. (The Times of India)
Group aspects: settlement department officials + local actors + complicity of records.
Malintegrity features: group collusion, manipulations hidden under legitimate administrative veneer.

Case 13: OBC bankers cheating case Mumbai

In Mumbai, two ex-bankers and three others were convicted (two-year jail) for defrauding the Oriental Bank of Commerce of ₹2.9 crore via manipulated accounts and shell companies—showing long-term group collusion. (The Times of India)
Group aspects: bank officers + directors of private companies + shell firms.
Malintegrity features: systemic manipulation, collusion, group capture of processes.

Case 14: CBI SEBI SPMCIL officers cases 2015‑2017

From public data: 15 cases were pending in the Department of Economic Affairs against officers of SEBI and SPMCIL under various charges. (Press Information Bureau)
Group aspects: regulatory-agency officers + internal division + external corporates.
Malintegrity features: group orientation of misconduct in regulation/printing, limited whistle-blowing, collusion.

Case 15: Granular data firm‑corruption perception India 2024

While not a single scandal, the research by Dutta (2024) on firm-level data in India found significant negative relationships between perceived corruption and economic freedom across states, implying networks and firm-level peer dynamics are at work. (Cambridge University Press & Assessment)
Group aspects: firms in networks, state agencies, peer behaviour across firms.
Malintegrity features: collective firm behaviour, peer influence, structural corruption beyond isolated acts.

7. Why Malintegrity Drives the “Most Amount” of Corruption

Here we extend your original arguments with the empirical and case-study evidence above.

7.1 Scale and contagion

When corruption is embedded in a group, behaviours spread. The modelling evidence shows that once a critical mass of corrupted actors exist, the majority can shift from honest to corrupt rapidly. Group contexts lower individual moral barriers, normalise misconduct and allow hiding of wrongdoing under veneer of legitimacy.
For example, in the Adarsh and Irrigation scam cases, entire clusters of actors (politicians, bureaucrats, contractors) were embedded in collusion networks that sustained misconduct over years. The firm-level data show perceptions of corruption spread across firms in certain states, indicating contagion across peer networks.

7.2 Institutional normalisation and invisibility

Malintegrity generates institutional inertia: because many actors are complicit, internal checks fail, whistle-blowing is suppressed, and oversight mechanisms may be co-opted or intimidated. This collective inertia allows corruption to persist undetected for longer, thus cumulatively causing more damage.
For instance, in the Haryana Forestry scam, whistle-blowers faced suppression; in the UP NRHM case, extreme retaliation (including alleged murders) shows how group complicity can make dissent extremely difficult. And the public data (CBI registrations) show only a partial tip of what may be happening underneath.

7.3 Collective rationalisation & peer pressure

In a group of actors, peer pressure, norms of loyalty, fear of ostracism, and rationalisation (“everyone does it”, “it’s the system”) make individuals more likely to remain silent or join corruption. The whistle-blower literature emphasises the retention of silence because of retaliation risk and group exclusion.
In many of the Indian cases above, group membership (politicians + bureaucrats + contractors) effectively created an environment where dissent is extremely hard and complicity becomes normative.

7.4 Weak whistle-blower channels & group complicity

Since malintegrity depends on group suppression or co-optation of whistle-blowers, the weaker the protections and the stronger the group loyalty, the more the corruption can proliferate. Empirical research shows that whistle-blowing legislation alone is not enough; cultural, structural and institutional factors matter.
For example, in the Adarsh case, though investigations eventually occurred, the delay was long and many actors remained unchallenged; in the Haryana Forestry case, the whistle-blower was himself sidelined. These show that group-based conspiracies thrive where internal check-points are weak and peer networks dominate.

8. Implications for Policy and Anti-Corruption Strategy

Given the malintegrity framework and expanded empirical grounding, the following implications arise (extending your original ones):

Focus on group-level dynamics: Anti-corruption efforts must target not just individual wrong-doers but networks, group norms, loyalties and incentives within organisations and institutions. For example, identifying hub actors in group networks (politicians, contractors, bureaucrats) and isolating them from their peer networks.
Strengthen whistle-blower culture & channels: Protecting individuals is necessary but not sufficient; creating cultures of accountability, safe channels, de-normalising loyalty to corrupt groups is vital. Implement anonymous reporting, protect whistle-blowers from group retaliation, monitor group-based peer pressure effect.
Restructure organisations to reduce collective capture: Models suggest that reducing group size, increasing transparency, varying salary dispersion, flattening incentive hierarchies, rotating staff across departments, and decentralising decision-making can reduce contagion of corruption.
Utilise network analysis: Use tools from network science (as seen in corruption scandal studies) to identify hubs and modules in corruption networks, not just individuals. Map organisational interaction networks to detect high-risk modules.
Promote external oversight and transparency: When internal group dynamics are skewed, external oversight (civil society, media, auditors) becomes critical to breaking malintegrity clusters. For example, public disclosure of all contracts, open databases of allocations, real-time audit logs, citizen-monitoring.
Change normative culture: Since group norms of silence, loyalty and complicity underpin malintegrity, cultural change efforts (training, leadership, ethics) should aim at reinforcing norms of speaking up and accountability. This means leadership modelling, peer-to-peer ethics programmes, reward for internal disclosures.
Leverage digital-governance tools to disrupt group collusion: Given the studies of IT and corruption in India, digitisation of tendering, procurement, payment systems can reduce the “offline group collusion” risk. But digital systems must be designed to anticipate how groups adapt or migrate their collusion. (IDEAS/RePEc)
Monitor data-trends and public datasets continuously: Using the public-data sources (CBI registrations, CPI scores, firm-level data) to monitor changes over time, detect emerging group-based corruption modules early, and evaluate interventions.

9. Limitations and Further Research

Measuring the hidden scale of malintegrity is challenging because it involves concealed wrongdoing and group complicity; available data is often retrospective and partial. Much modelling is theoretical/agent-based; empirical large-scale longitudinal data on group corruption dynamics remains limited. Further research is needed on how digital/social technologies affect group corruption and malintegrity (e.g., online peer networks, whistle-blower anonymity). Cross-cultural differences in group loyalty, whistle-blowing norms, and institutional protections need deeper examination. In the Indian context specifically, state-level variation in group networks, political-bureaucratic collusion and the role of informal systems (patronage, kin networks) merit further study.

10. Conclusion

This paper has argued that malintegrity—the group-based corrupted state of integrity sustained through collective silence, complicity, and group norms—drives much of the world’s most damaging corruption. By focusing on the group rather than just the individual, we uncover how peer-networks, loyalty, fear of whistle-blowing and structural incentives combine to enable large-scale, persistent, hidden wrongdoing.

Our expanded empirical grounding — data trends, public-data sources, and Indian case-studies — strengthens this argument, showing that group dynamics are not a fringe phenomenon but core to the corruption challenge. Addressing corruption effectively therefore demands strategies targeting group dynamics, promoting whistle-blower culture, restructuring organisations, deploying network-analysis to identify corrupt clusters, and utilising digital transparency to disrupt collusion. In doing so it becomes possible to turn the tide on the vast and deeply entrenched phenomenon of malintegrity.

References

(You will need to complete full bibliographic entries for all works cited including the 15 case-studies and the modelling/network literature.)

Chassang, S., “Corruption, Intimidation, and Whistle-blowing.” NBER Working Paper.
Du, Q., “Political corruption, Dodd–Frank whistleblowing, and …” (2022)
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Vian, T., “Whistle-blowing as an anti-corruption strategy…” (2022)
Valverde, P. et al., “Controlling systemic corruption through group size…” (2023)
Nekovee, M., Pinto, J., “Modelling the Impact of Organization Structure…” (2017)
Bretón-Fuertes, E., et al., “Explosive adoption of corrupt behaviours…” (2025)
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Rahman, K., “Overview of corruption and anti-corruption developments in India.” (2022) (Knowledge Hub)
Dutta, N., “Perceived corruption, economic freedom, and firms in India.” (2024) (Cambridge University Press & Assessment)
Mukherjee, N., Sah, R., “Corruption and Economic Growth: A Correlation Study for India.” (2021) (ijpsl.in)
Sakuntala, S. Sri et al., “The complexity of corruption and recent trends in information technology for combating corruption in India.” (2024) (IDEAS/RePEc)
Data.gov.in: Year-wise detail of corruption cases registered by the CBI (2017-2021) (Data.gov India)
Transparency International: India profile and CPI score. (Transparency.org)

Sunday, October 19, 2025