Case Study: Reputation Risk Management

International bank caught in a money laundering scandal

NEED: Crisis Management

PROBLEM: Reputational Risk

SOLUTION: Social Media Listening (Twitter scrape)

Analyzing tweets about the scandal. Tweets scraped based on localization and languages. Extracting the main topics discussed and how emotional people are about them. Topics, sentiments analyses and comparative analyses between countries and topics. Designing the most fitting communication plan based on the analyses’ findings. 99,99% statistical accuracy.

This case study aims at deepening your understanding of what to expect from SecurAI’s services. It is based on real-life events and data. The elements that would allow the reader to identify the bank and events in question have been redacted. The international bank will be referred to as IntBank. The geographical area affected by the sandal will be referred to as REGION. REGION is formed by three countries, referred to as COUNTONE, COUNTTWO and COUNTTHREE. Four languages are in use in REGION.
The scandal mostly revolves around 3 customers of IntBank. 3 companies operating from Crimea, and yet having active accounts in COUNTTWO. Moreover, those 3 customers generated large volumes of internet transactions all over REGION. So large, that it added up to a substantial portion of IntBank’s business in REGION. This leveled the question: was IntBank somehow involved willingly in the money laundering?

SecurAI worked on understanding and identifying the aftermath of the scandal in order to draw an action plan to salvage IntBank’s reputation, brand image and customer relationship.
How? By scraping tweets containing references to “money laundering” and “IntBank”. SecurAI proceeded as follows.
- Only tweets in one of the four languages in use in REGION and localised - thanks to IP addresses - within COUNTTWO, COUNTTWO or COUNTTHREE were collected. 
- 6754 fitting tweets were found and extracted.
- The tweets were translated into a common language. This procedure enables analyzing the data on an aggregated level as well as between countries. Thanks to the NLP capabilities of DrAI, the tweets were easily and quickly translated to English. English is the chosen default language used by SecurAI to process and analyze data.
- The 6754 tweets were screened and cleaned of any duplicated Tweets, nonsense Tweets, and other unreliable, unprocessable, irrelevant data. Thanks to the NLP capabilities of SecurAI and its artificial intelligence, the cleaning process - backed by our data analysts - is reliable, quick and accurate.
- 2290 tweets remained and were finally ready for analysis. Topics analyses were conducted - meaning the SecurAI’s AI “read” through all of the tweets to figure out what is being said about IntBank and the money laundering scandal.  
- Once the tweets understood, recurrent or related topics were identified. 3 main conversations - category topics/topic groups - emerged from the unstructured mass:
a. Concerns about Bankers’ morality;
b. Concerns about Financial Automation and digitization in relation to fraud;
c. Concerns specific to people from COUNTTWO.
- Sentiments analyses were then run to compare sentiments’ types and intensity between topic groups as well as between countries.

Here under a glimpse at the outcome of those analyses: statistical results, interpretations and recommendations from SecurAI based on those.

Overall results

Overall, there’s a good deal of anger among consumers.

However, trust and anticipation are the dominating sentiments.IntBank is in the enviable position of having built a substantial reservoir of trust, IntBank should build on it to reassure the customers.

Yes, people are angry about the money laundering news. But they also anticipate that there will be changes in how the bank operates. Anticipations need to be met. Disclosing the plan of action to remediate the money laundering practices would be a good move. A high level of transparency will be essential. No customers want to think their banker is a crook, but they also understand that there can be bad apples. The results of the investigation should be shared with the public to the extent possible within the constraints of privacy policies.
Nevertheless, IntBank’s customers are extremely knowledgeable about what has happened. There is no room for vague promises.

IntBank’s external communication should also take into account country’s specificities. There are significant differences in the emotional nature of the tweets based on the nationality of the writer. Overall, COUNTTWO nationals showed significantly higher levels of negative & positive emotions in their tweets.

They are more emotional. The scandal hit harder there. COUNTTWO nationals also showed significantly higher levels of anticipation: communicating on the action plan is of utmost importance there.

a. Concerns about Bankers’ morality

This topic category scores higher than the other two in terms of expressions of surprise and significantly higher in terms of fear.

Overall this category scores highest on negative feelings: banking malfeasance - especially when involving large banks - are decidedly unpopular.

“About 40 billion dollars in dirty money [...] reveals IntBank's special  [love of] high-risk customers”
“The first conclusion about IntBank's two-faced business is perhaps that, with this money laundering scandal, the worst is still ahead.”

People from REGION tweeting about the scandal wonder: How is it that companies operating in the Crimea could come to be laundering money through a bank in COUNTONE or COUNTTHREE? Didn't they have to open an account in person like anyone else, and didn't someone notice something out of the ordinary? Is IntBank willingly accomplice?
They hope that an intelligently run investigation will get to the bottom of it and lead to profound remediations.

People who put money in a bank are more than customers; they are more like partners. Trust in this kind of intimate relationship requires hard work and clear communications. Who is being held accountable, how?
That is what IntBank should focus on to appeal again to that share of his customer and calm the public opinion.

b. Concerns about Financial Automation and digitization in relation to fraud

The scandal triggered a broader conversation around modern banking. It revealed that customers fear automation. Indeed, they express the feeling of being let down by the technology - while apparently the same tech is enabling the malicious conducts such as money laundering.

This topic category has the lowest level of trust. This should be of particular concern. The fact that, especially in COUNTTHREE, the banking scandal was quite overt, might explain this. Insiders from a competitive bank had raised concerns years before, but no action was ever taken.

Many of the Tweets in this category are from banks themselves, notifications revolving around those lines:

“ technical problems in the operation of Smart-ID” “Interference with Smart-ID currently detected”

But the main bulk of the Tweets is directly about money-laundering.

“In Russia, the Exsministri transferred hundreds of millions of euro from the COUNTTHREE IntBank to an offshore area"
“Only 100 billion washed away in REGION.”

c. Concerns specific to people from COUNTTWO

This topic reflects concerns individuals have about the activity of Intbank within COUNTTWO, where many Tweets identify the bank as having been engaged in malfeasance.

Money laundering transactions in COUNTTWO are seen as a real problem on which IntBank had a hand.

“IntBank deals with chemical weapons program in Syria, mention COUNTTWO".
“Did the IntBank scandal at the expense of COUNTTWO local shareholders lead to Donald Trump's [situation]?

IntBank is the largest bank of COUNTTWO, with almost 1 million individual clients and 83,000 business clients. Customers seem to look at transactions as if they happen by magic or are caused by a conjurer. Payments are made, money is sent to and from, with what seems to them as little oversight or monitoring.

According to the law firm close to the case, "IntBank COUNTTWO customers used an online banking platform to initiate 522 outgoing transactions, totaling approximately $4.43 million." - money laundering transactions.

If you want to learn more about SecurAI’s capabilities and service, you can book a talk to one of our experts by booking a demo and discussing your unique case and specific needs.

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