A Comprehensive Guide to Alleviating Data Fabrication

How Knowledge Graphs Help Alleviate Data Fabrication

What is Data Fabrication?

Data fabrication is an intentional misrepresentation of research findings. It’s a growing concern in today’s digital age. With the increasing amount of data being generated and collected, it has become easier for individuals and organizations to manipulate data for their benefit.

Data fabrication in drug trials and clinical research is a gateway for more funding, gaining popularity among the masses, and establishing credibility in the industry.
In the business world, it’s helpful to market the company and its results if we exaggerate the numbers. After all, it’s common knowledge that numbers are essential to the success of the product, service, or technology because it ultimately gets more business.

Why do researchers commit fraud?

• Pressure on researchers to publish academic papers to get tenure or funding.
• Precious funding resources get exhausted.
• Insufficient empirical evidence.
• No peer supervision, no set standards, and poor decision-making.
• Bias, personal prejudice, and poor judgment.
• Plagiarism

This can have serious consequences, especially in fields such as healthcare and finance, where decisions based on inaccurate data can have life-changing consequences.

Take the example of Andrew Wakefield, a British activist and former physician who played a major part in the anti-vaccination propaganda in 1998, publishing a study in the Lancet, a weekly peer-reviewed general medical journal. He indicated a connection between autism and the measles-mumps-rubella vaccine. The study received widespread attention and lead to many patients refusing the vaccine leading to widespread outbreaks of the disease in Europe. When faced with an investigation in 2010, it was discovered that he and his colleagues had altered the facts about the children in their study. He was paid off by a lawyer who was planning to sue the manufacturer of the vaccine. He was found guilty by the British General Medical Council for fraud and misconduct.

Fortunately, knowledge graphs offer a solution to this problem. In a knowledge graph, information is stored in a way that makes it easy to see relationships and connections between different pieces of data.

Detecting Inconsistencies and Anomalies and their use case in Medical Research and Drug Discovery

When a medical professional falsifies information about a patient’s medical history, it can lead to discrepancies in the knowledge graph. The patient’s medical history is closely linked to other data points, such as current medications and previous diagnoses. Graphs can analyze patients, health conditions, medical records, and treatments as different entities and suggest treatments and improve decision making and optimize patient health.

Any attempt to alter this data would lead to discrepancies that can be detected immediately

Motives for Data Fabrication in Clinical Research:

• Academic Recognition: Researchers may fabricate data to enhance their reputation, secure funding, publish in highly regarded journals, or advance their academic careers.
• Financial Interests: Pharmaceutical companies or individuals may manipulate data to support the efficacy or safety of a particular drug or treatment.
• Career Advancement: Data fabrication driven by personal ambitions, such as obtaining research funding, tenure, or career progression.

Consequences of Data Fabrication:

• Misleading Scientific Community: Fabricated data can mislead the scientific community, resulting in wasted resources, misguided research, and potentially harmful treatments.
• Jeopardizing Patient Safety: Reliance on falsified data may lead to the approval and use of drugs or treatments that are ineffective, unsafe, or have unforeseen side effects, endangering patient health.
• Damage to Scientific Integrity: Data fabrication undermines the credibility and trust in scientific research, making it difficult to replicate results and eroding public confidence.
• Legal and Professional Consequences: Researchers caught fabricating data may face severe penalties, such as loss of funding, retraction of publications, loss of credibility, and disciplinary actions.

Preventive Measures:

• Ethical Guidelines: Researchers should adhere to ethical guidelines and codes of conduct, such as those provided by regulatory bodies, research institutions, and professional organizations.
• Rigorous Review Process: Journal editors and peer reviewers should thoroughly evaluate submitted research papers, scrutinizing data and methodology to identify suspicious patterns or inconsistencies.
• Data Transparency and Reproducibility: Encourage open data practices, data sharing, and reproducibility of research findings to promote transparency and accountability.
• Education and Training: Promote ethics training and awareness among researchers, emphasizing the importance of integrity, responsible conduct of research, and the consequences of data fabrication.

Using Knowledge Graphs in alleviating data fabrication in clinical research, medical trials, and drug discovery.

• Data integrity and transparency: Knowledge graphs maintain data integrity and provide transparent records, discouraging data fabrication.
• Audit trails: Detailed audit trails in knowledge graphs create a reliable record of data changes, making fabrication challenging to conceal.
• Validation and consistency: Knowledge graphs validate data against rules, ensuring accuracy and reducing the likelihood of fabrication.
• Traceability and provenance: Knowledge graphs capture data lineage, origin, and dependencies, facilitating the detection of fabrication attempts.
• Collaborative verification: Multiple stakeholders can validate data within knowledge graphs, enhancing integrity through collective efforts.
• Cross-referencing and verification: Linking data from various sources helps identify inconsistencies and fabrication across datasets.
• Real-time monitoring: Continuous monitoring of data updates aids in detecting suspicious activities and preventing fabrication.
• Data governance and ethics: Integrating knowledge graphs with governance frameworks and ethical guidelines promote responsible data handling, mitigating the risk of fabrication.

Ensuring Data Accuracy and Integrity And its use case in the BFSI sector.

A benefit of knowledge graphs is their ability to ensure data accuracy and integrity. By emphasizing the associations and linkages among data, knowledge graphs effectively prevent data manipulation without detection. This helps to ensure that data is accurate and trustworthy, which is especially important in fields such as healthcare and finance.

For example, in the field of finance, knowledge graphs can be used to detect fraudulent activity. By analyzing patterns and connections within the data, knowledge graphs can identify suspicious behavior that may indicate fraud. This helps ensure that financial data is accurate and trustworthy, essential for making informed decisions.

Motives for Data Fabrication in BFSI :

• Financial Gain: Fabricated data can be used to manipulate financial reports, inflate assets, understate liabilities, or misrepresent profitability to deceive investors and stakeholders.
• Fraudulent Activities: Fabricated data can be employed to perpetrate fraud, such as identity theft, account takeover, or unauthorized fund transfers.
• Regulatory Evasion: Manipulated data can help evade regulatory requirements, such as reporting obligations, capital adequacy ratios, or compliance with anti-money laundering (AML) and Know Your Customer (KYC) regulations.

Consequences of Data Fabrication:

• Financial Losses: Fabricated data can lead to incorrect financial decisions, investments, or assessments, resulting in substantial financial losses for individuals, organizations, or investors.
• Legal Penalties: Data fabrication is illegal and can lead to criminal charges, fines, lawsuits, and imprisonment for the individuals or entities involved.
• Reputational Damage: Instances of data fabrication can severely impact the reputation and credibility of financial institutions, leading to the loss of customers, investors, and business opportunities.
• Regulatory Action: Regulatory bodies can impose sanctions, license revocation, or heightened scrutiny on institutions found guilty of data fabrication.

Preventive Measures

• Strong Internal Controls: Implement robust internal controls, segregation of duties, and regular audits to detect and prevent data fabrication.
• Data Governance: Establish comprehensive data governance policies, including data quality checks, data validation, and encryption protocols.
• Employee Awareness and Training: Educate employees about the risks and consequences of data fabrication and provide training on ethics, compliance, and fraud detection.
• Regulatory Compliance: Ensure compliance with industry regulations and standards, such as Basel III, GDPR, PCI-DSS, and AML/KYC guidelines.
• Whistleblower Protection: Encourage a culture of transparency and provide mechanisms for employees and stakeholders to report suspicious activities without fear of retaliation.

Using Knowledge Graphs to Prevent Fraud in Banking and Finance Sector:

• Entity linkage and identity resolution: Linking data to detect and prevent identity theft and account manipulation.
• Fraud pattern detection: Identifying complex patterns of fraudulent activities.
• Real-time monitoring and alerts: Promptly detecting anomalies and generating timely alerts.
• Risk assessment and fraud predictions: Assessing risk and predicting potential fraud.
• Compliance and regulatory support: Ensuring adherence to regulations and identifying compliance gaps.
• Cross-institution collaboration: Collaborating to combat fraud schemes across multiple organizations.

Conclusion

In conclusion, knowledge graphs offer a powerful tool for alleviating data fabrication. By storing data in a way that highlights relationships and connections, knowledge graphs make it difficult for individuals to manipulate data without being detected. This ensures that data is accurate and trustworthy, which is essential for making informed decisions in areas such as healthcare and finance. As the amount of data generated and collected grows, knowledge graphs are becoming an increasingly important tool for ensuring the accuracy and integrity of data.

Citations

1. Dr. Shruti Mantri, Vishal Siram (2020).Knowledge Graph: Facilitates Fraud Analytics.Retrieved from https://isb-institute-of-data-science.medium.com/knowledge-graph-for-financial-services-c9cb7c3fe2b9
2. Precision Health, https://www.youtube.com/watch?v=qmdvzMk_k3w
3. Ashwaria Gupta, (2013).Fraud and misconduct in clinical research: A concern. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3700330/
4. Dan Ariely (2012).Understanding the Causes. Retrieved from https://www.ncbi.nlm.nih.gov/books/NBK475947/#:~:text=A%20range%20of%20possible%20reasons,larger%20pattern%20of%20social%20deviance.
5. T. S. Sathyanarayana Rao and Chittaranjan Andrade,(2011).The MMR vaccine and autism: Sensation, refutation, retraction, and fraud. Retrieved from https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3136032/