The convenience offered through digital or real-time payments has been shadowed by the frequent occurrence of fraudulent transactions, reducing the user’s experience and confidence in utilising the mode of payment. Every threat has a beginning that, when left unattended, evolves into a big challenge in the future. This highlights the importance of screening merchants or their onboarding process, which includes risk scoring their profile, business portfolio, and associates. Wondering how an intricate process like merchant screening can be performed in a detailed way that identifies politically exposed persons (PEP) or illicit profiles of institutions? Employing adverse media screening can be a proactive approach for banks and financial institutions struggling to identify negative news about individuals or businesses involved in any financial misconduct, criminal investigations, or other potential breach activity.

While onboarding merchants is a vital process, ensuring their profiles are legitimate requires additional diligence. Beyond just using risk scoring, adverse media screening adds a critical layer of protection, helping banks stay updated on the latest industry developments and minimising the risk of onboarding high-risk or non-compliant merchants.

This blog explores the challenges banks and fintechs face in adopting adverse media screening. It also covers best practices and the key components of an effective screening model. Lastly, it highlights how BANKiQ provides essential support in implementing a comprehensive adverse media screening solution.

Adverse Media Screening: An Overview

As the name suggests, adverse media screening is a systematic process for screening or checking adverse media for mentions of potential individuals and entities tied to negative or illegal events, such as anti-money laundering activities, financial misconduct, and fraudulent transactions. This proactive screening serves as a robust payment protection framework for banks and financial institutions in identifying risky profiles in advance, thereby preventing fraud, ill-legal threats, and data breaches. Adverse media screening functions by collecting large volumes of data from various sources, such as news websites, media channels, government portals, and forums. This facilitates the early detection and categorisation of profiles based on the severity of the adverse media.

The significance of adverse media screening is vast. However, any modern technology that assists in identifying fraudulent transactions faces various challenges, which need to be addressed first to fully realise its significance. So, let’s recognise and overcome the roadblocks to employing an adverse media screening solution.

Challenges of Adverse Media Screening

Data Quality and Reliability

Adverse media screening involves analysing vast datasets from various public sources, where data quality is often a concern. The reliability of this information frequently needs verification, as misinformation or biased reporting can influence adverse media results. Given the critical role data plays in the screening process, it is essential to obtain and ensure clear, accurate, and transparent information to ensure an effective adverse media screening solution.

Data Overload

Banks and fintech companies already manage large volumes of data from transactions and customer details. Adding data from multiple adverse media sources increases the data load, making it harder for professionals to focus on key information. Even automated tools struggle to efficiently analyse and identify relevant adverse news within this overwhelming data. Thus presenting a significant challenge for both fintech and banks.

False Positives

Poor data quality and misinformation significantly increase the likelihood of adverse media screening solutions flagging incorrect profiles. Whenever a name is mentioned, regardless of the individual’s actual involvement in the adverse news, the system is likely to flag it, resulting in false positives. This underscores the importance of incorporating manual review into the process.

Privacy and Compliance Concerns

Collecting and analysing data on individuals or institutions raises privacy concerns, especially in regions like the European Union, where strict regulations govern the handling of sensitive information. Failure to comply with these measures can result in severe penalties for banks and financial institutions. Additionally, it is crucial to ensure that data is used solely for lawful purposes by obtaining confirmation from relevant legal parties.

Integration with Existing Systems

Integrating an adverse media screening solution into existing compliance, onboarding risk scoring, and payment protection systems can be a complex task for banks and financial institutions. The setup and configuration adjustments demand significant time and resources, making it particularly costly for small and medium-sized financial institutions.

Best Practices  for Implementing Adverse Media Screening

Certain strategies need to be adopted to overcome the challenges of employing adverse media screening. These strategies help ensure the solution’s effective and efficient operation.

Establish

Establish Clear Policies

  • As a primary step, it is essential to highlight the purpose and scope of the adverse media screening process. This will help the solution understand the requirements of banks and financial institutions, such as identifying risky profiles of individuals or entities, flagging profiles associated with anti-money laundering activities, or protecting the organisation’s reputation.
  • As a next step, outline the working procedure of the adverse media screening solution regarding data collection, flagging frequency, the criteria for flagging a profile, escalation procedures, and reporting structure. Thus, limit the flagging of false positives.

Leverage Advanced Technology

  • Integrating advanced technologies such as AI and ML tools into adverse media screening helps handle large volumes of data from multiple sources with ease. It also facilitates real-time monitoring of news, such as scanning for mentions of individuals, entities, or businesses involved in any fraudulent activity.
  • With AI and ML algorithms, the adverse media solution can learn from past screening ability, where the solution becomes accurate enough to differentiate the mentions from a causal one to a warning sign. This ability to learn and differentiate between relevant and irrelevant information helps the solution reduce unnecessary flags.
  • AI-based tools automate the process of collecting and analysing data, accelerating adverse media screening. This enables swift information evaluation and risk score assignment, allowing banks and financial institutions to prioritise cases based on severity.

Customise Screening Parameters

  • Aligning the adverse media screening solution according to your organisational needs helps you understand the nature of your business. For example, banks are focused on news regarding fraud and data breach activities. By customising the screening parameters, the adverse media screening solution brings the risks that are most relevant to the business.
  • With different regulatory requirements, a tailored adverse screening solution helps meet the data privacy law and compliance requirements of that region, thereby avoiding repercussions and penalties.
  • Customisation allows banks to focus on data sources that are most pertinent to their service. For example, global fintech might screen international media, while regional banks may limit screening to local news.
  • Also, with tailoring screening parameters, banks and financial institutions can easily adjust their screening criteria to ensure they remain relevant. This ensures that the screening process evolves with the organisation’s risk environment.

Incorporate Human Oversight

  • Though integrating advanced technology expedites the process, human interference helps in contextual analysis and the risk level of the sensed adverse media, ensuring the screening does not flag irrelevant information or miss important news.
  • In the event of false positives, human oversight allows for manual revision of the flagged individual or entity, eliminating unwanted triggers or the wrong person.
  • With a manual reviewer, banks can evaluate incomplete information by cross-referencing multiple sources or checking background information not captured by the screening tool.

How Does Adverse Media Screening Work?

  1. Data Collection : This marks the first step in adverse media screening, where various information is collected from public sources such as forums, national websites, and news articles to check the reliability and accuracy of the listed adverse media. Each resource gives a different angle to the adverse media, which is essential in checking the reliability of the information.
  2. Data Analysis : In this step, the adverse media utilises two key components, namely, automated screening and risk scoring, to analyse the collected information. The automated screening leverages advanced machine learning algorithms to perform heuristic analysis of the collected data, scanning through the diverse data set for mentions of any adverse news or keywords related to fraud, corruption, or legal actions. Also, the algorithms are programmed so that any mentions of keywords such as money laundering or terror financing are immediately flagged.Once the adverse media has been identified, each flagged mention is evaluated for its severity, and a risk score is allocated. Risk scoring goes beyond simple keyword detection and considers the context in which adverse information appears. For instance, if an individual is briefly mentioned in a negative news article but not directly involved in wrongdoing, the risk score might be lower than for someone who is a key subject of multiple adverse reports.
  3. Flag & Alerts : The flagging mechanism in adverse media screening functions is based on predefined rules set by banks or financial institutions. For example, rules might specify that any profile connected to a Politically Exposed Person (PEP) or involved in negative news related to fraud must be flagged for further review. This helps banks and fintech companies avoid falling to immediate conclusions. The real-time alert generated by the adverse screening solution allows financial institutions to act swiftly on high-risk cases, reducing the chance of undetected fraud or regulatory breaches.
  4. Integration with Existing Systems : The flagged profiles, risk scores, and alerts generated are then shared across customer due diligence and KYC solutions without disrupting workflow continuity. Thus facilitating a streamlined compliance process with reduced errors. Also, a centralised dashboard is made available where the compliance teams are provided with a clear view of all flagged cases, associated risk scores, and audit trails, tracking and managing risks efficiently while also maintaining records for internal reviews and regulatory audits.
  5. Continuous Improvement : As a final step, the adverse media screening solution harnesses a feedback system that constantly learns from past screening outcomes, adjusting algorithms and workflows based on identified patterns, false positives, or emerging risks. This helps the system refine and enhance its working process to be accurate and responsive to new threats.

Thus, the working model of an adverse media screening solution demonstrates a multi-faceted approach that ensures no adverse mentions or media go unnoticed.

Introduction to BANKiQ

BANKiQ is a modern-age fraud prevention platform that significantly enhances adverse media screening through its Intelligent Anti-Money Laundering (IAML) solution. By leveraging cognitive AI and machine learning, BANKiQ monitors customer behaviour to identify unusual financial transactions. It analyses extensive data, including adverse media reports, to facilitate a streamlined merchant onboarding process. Merchants are screened against sanctioned and blocked lists, ensuring that potential risks related to money laundering and financial crimes are addressed early on, allowing banks and fintechs to adopt a proactive stance in their risk management efforts.

In today’s digital landscape, proactive and preventive measures are more crucial than ever, and BANKiQ simplifies this process. While not all merchants engage in illegal activities, a thorough screening of every merchant list is essential. BANKiQ’s merchant onboarding risk scoring and Intelligent Anti-Money Laundering (IAML) solutions provide the perfect framework for ensuring compliance and mitigating risks effectively.

Conclusion

As financial crimes evolve, adopting advanced preventive solutions is critical. Adverse media screening plays a vital role by helping financial institutions identify risks that may otherwise be overlooked during onboarding. Systematically analysing adverse media reports enables better compliance management and enhances risk mitigation strategies. BANKiQ’s Intelligent Anti-Money Laundering (IAML) solution aids in this by using cognitive AI and machine learning for efficient screening.

Its proactive approach ensures banks and fintechs can detect potential risks early, safeguarding operations and reputation in today’s complex financial landscape. Embracing such innovations is crucial for navigating the complex financial landscape.

Stay ahead and advanced with BANKiQ, the modern-age fraud detection platform.

Recommended Posts