Silent Customer Churn: How AI Can Save Haitian Businesses
In Haiti, banks, telecommunications companies, fintechs, and numerous institutions face a discreet yet profoundly costly phenomenon: thousands of customers stop using their services without complaining, without seeking assistance, and without showing the slightest sign of formal dissatisfaction.
They withdraw silently, often after a series of low-visibility behaviors. This dynamic, known as silent customer churn, represents one of the most significant losses for Haitian businesses, though its impact remains largely underestimated.
The real cost? Difficult to quantify precisely, but experts estimate that acquiring a new customer costs between five and seven times more than retaining an existing one. For an average Haitian bank, this could represent millions of gourdes lost each year, simply because warning signs go unnoticed.
A Culture of Acquisition Without Retention
This situation stems first from a culture that places disproportionate emphasis on acquiring new customers, at the expense of retention. Many institutions focus on growing their customer base without investing in understanding those who are already engaged.
In the Haitian banking sector, for example, marketing campaigns to attract new customers are omnipresent. We see advertisements everywhere: on the radio, on social media, in the streets. But how many of these institutions invest as much energy in understanding why a customer who regularly used their checking account has stopped making transactions? Why has a loyal customer of three years suddenly stopped using their banking services?
Yet, it is well established that a retained customer represents significantly greater value than a replaced customer. A loyal customer not only uses more services over time, but also recommends the institution to their network. Ignoring this truth is tantamount to letting fundamental value slip away.
Rich Data Left Unexploited
The lack of visibility on this phenomenon also stems from the underutilization of internal data. Haitian banks and businesses possess transactional histories, usage behaviors, customer interactions, and socioeconomic data.
Every transaction, every customer service call, every mobile app login generates valuable data. A customer who went from five transactions per week to just one per month. Another who regularly called customer service and has stopped. A third whose monthly deposits are progressively decreasing. All these signals tell a story—that of progressive disengagement leading inevitably to abandonment.
This information should enable the identification of disengagement risks, but it often remains unexploited. It accumulates in disparate systems, without being analyzed or transformed into decision intelligence. Institutions possess a goldmine of information but lack the tools to extract its value.
The problem doesn't lie in the quantity of available data, but in the absence of capacity to interpret it intelligently and in real-time.
The Absence of Intelligent Tools
The absence of intelligent tools in internal structures constitutes another major weakness. Without systems capable of identifying the progressive decrease in interactions, activity drops, periods of frustration or irregularity, disengagement remains invisible until it's too late.
Customer relations teams often work with basic Excel spreadsheets, monthly reports that show overall figures but lack granularity. By the time they finally notice that a customer segment has declined, the departure is already done. Companies then find themselves facing irreversible situations that could have been anticipated.
Imagine an account manager who receives each morning a list of at-risk customers, with clear explanations of the probable reasons for their disengagement and specific recommendations to win them back. It's technically possible, but few Haitian institutions have taken this step.
Interventions Without Strategy
Another challenge lies in the absence of intervention automation. Even when a risk is detected, teams don't have access to clear, immediate recommendations adapted to the local reality.
They often move forward blindly, without understanding the probable motivations for departure and without a precise strategy to win back the customer. Customer relationship management becomes a reactive exercise rather than a preventive one. We call the customer after their departure to ask why they left, instead of intervening before they reach the point of no return.
This reactive approach is costly. First, because it's more difficult to win back a departed customer than to retain one on the verge of leaving. Second, because each lost customer represents not only a loss of revenue, but also a potential negative ambassador who will share their dissatisfaction with their network.
In the Haitian context where word-of-mouth plays a crucial role in financial decisions, the impact can be devastating.
A Historic Event: Ayiti AI Hackathon
It was in this context that a historic event for the Haitian technological community took place. Ayiti AI organized a hackathon entirely dedicated to artificial intelligence, marking a major first in the country.
The initiative brought together technology enthusiasts, experts, developers, students, and professionals motivated to build innovative solutions. For forty-eight hours, from November 28 to 30, 2025, eleven teams worked relentlessly to develop artificial intelligence applications addressing concrete Haitian problems.
The event benefited from the support of several influential institutions such as the Bank of the Republic of Haiti, Haiti AI Grant, Transversal, ElevenLabs, IBC Express Shipping, Shneiderson Immobilier, the École Supérieure d'Infotronique d'Haïti (ESIH), and many others who contributed significantly to its success.
This mobilization testifies to a collective will to accelerate local innovation. It also shows that the private sector, academic institutions, and the Haitian tech community can collaborate effectively when the objective is clear and the vision is shared.
BankChurnAI Agent: A Haitian Solution for a Haitian Problem
It was within the framework of this event that I participated as captain of team IMPACTIS. Our team consisted of three developers sharing a common conviction: Haitian problems deserve Haitian solutions, designed by people who intimately understand the local context.
During forty-eight hours of intensive work, between coffee, late-night debugging sessions, and moments of doubt, we developed BankChurnAI Agent, the first Haitian artificial intelligence agent designed specifically for the country's banks.
Why Focus Specifically on the Haitian Banking Sector?
Available indicators suggest this is one of the areas where the urgency of the problem and the potential for impact converge most significantly.
On one hand, the silent churn phenomenon appears particularly concerning here, as evidenced by the low banking penetration rate and the stagnation of the annual deposit growth rate observed in 2018 (BRH, Annual Report 2018). These elements reflect chronic underservice and persistent difficulty in retaining and durably engaging clientele, constituting a major obstacle to financial inclusion, which the BRH has identified as an essential lever for economic growth.
On the other hand, the potential impact of a technological solution is considerable. Haitian financial institutions have established operational structures and generate a growing volume of digital transactional data, particularly through the development of bank cards and electronic money. This data constitutes strategic raw material for deploying artificial intelligence solutions focused on behavioral analysis and customer retention.
Finally, the need for action is urgent. As a pillar of the economic system, the stability of the banking sector and its capacity to finance businesses, particularly SMEs in accordance with BRH guidelines, directly depends on its ability to better understand, anticipate, and retain its clientele in a context marked by the pursuit of increased resilience and efficiency.
This experience confirmed for me that technological solutions capable of addressing local challenges can be created here, by mobilizing the country's intellectual and creative resources. We don't need to wait for a solution to be developed elsewhere and then attempt to clumsily adapt it to our context. We can build, here and now.
How It Works: Technology in Service of Retention
BankChurnAI Agent precisely analyzes transactional and relational behaviors to anticipate customer churn. But how does it work concretely?
Detecting Weak Signals
The agent identifies at-risk users by detecting weak signals that human teams often don't have the time or tools to spot. These signals include:
- Progressive decrease in transaction frequency
- Changes in average transaction amounts
- Increasing gaps between mobile app logins
- Negative interactions with customer service
- Behavioral comparisons with customers who left in the past
Artificial intelligence excels at this type of analysis because it can process thousands of variables simultaneously and identify complex patterns that the human eye cannot perceive.
Transparency and Explanations
The agent offers essential transparency by clearly explaining the possible reasons for an imminent departure. Rather than simply saying "this customer is at risk," it explains why: "This customer has reduced their transactions by 65% over the last two months, a pattern similar to that observed in 87% of customers who closed their accounts."
This transparency is crucial for two reasons. First, it allows teams to understand and trust the AI's recommendations. Second, it guides interventions by identifying the probable cause of disengagement.
Contextualized Recommendations
The agent then proposes marketing and financial recommendations adapted to the Haitian context and formulated in Creole or French. This linguistic and cultural adaptation isn't a cosmetic detail, it's fundamental.
For example, the agent won't simply recommend "offering a discount on transaction fees." Instead, it will suggest: "Contact the customer in Creole, explain the advantages of the new digital cash program, and offer them a personalized session to show them how to use the app more easily."
These recommendations are immediately actionable and enable the application of necessary actions to preserve customer relationships.
A Potential for Deep Transformation
The adoption of a technology like BankChurnAI Agent could profoundly transform how institutions manage customer relationships.
Measurable Loss Reduction
It would enable a notable reduction in losses. If a bank loses 10% of its customers each year due to silent churn, and an AI solution can identify and retain even half of these at-risk customers, the financial impact is considerable. For an institution with 50,000 customers, this could represent 2,500 retained customers, potentially generating millions of gourdes in preserved revenue.
Improved Customer Satisfaction
Beyond the numbers, the impact on customer satisfaction is equally important. A customer who receives personalized attention before even thinking of leaving feels valued. They understand that the institution truly cares about them, not just their money.
This proactive approach builds trust and loyalty in ways that traditional loyalty programs cannot match.
Consistency and Operational Efficiency
The agent also improves decision consistency. All at-risk customers are identified according to the same objective criteria. Recommendations are based on data and proven patterns, not on an account manager's variable intuition.
Teams become more efficient because they know where to focus their efforts. Instead of treating all customers the same way, they can prioritize those who truly need it.
Substantial Savings
Finally, the generated savings are substantial. Fewer lost customers means less need to invest massively in acquiring new customers to compensate for attrition. Marketing resources can be reallocated toward service improvement and new product development.
Companies would thus have a tool capable of transforming reactive management into a preventive strategy, creating a virtuous circle of satisfaction and profitability.
The Moment to Decide: AI Is No Longer Optional
The challenge for Haitian businesses is now to embrace a transition toward a culture of analysis and anticipation.
The question is no longer whether artificial intelligence is applicable to the Haitian context. The Ayiti AI hackathon has irrefutably demonstrated that yes, it is. Haitian developers built in forty-eight hours solutions that work and address real needs.
The real question now is: which institutions will have the courage and vision to integrate these technologies before others?
Those who make this choice will benefit from a considerable strategic advantage in an increasingly competitive market. They will understand their customers better, serve them better, and build lasting loyalty in a sector where trust is the most precious asset.
The laggards will find themselves catching up while their more agile competitors capture the value. In the modern business world, the first move counts enormously.
A Technological Turning Point for Haiti
This hackathon organized by Ayiti AI marks a turning point in the country's technological history. It demonstrates that innovation can emerge locally, in a structured and collaborative manner.
Beyond the technical solutions produced, the event created something perhaps even more precious: a community. Developers who didn't know each other a few weeks ago are now collaborating on projects. International mentors from Google, Meta, and Harvard have shared their expertise. Financial institutions have seen concretely what AI can do.
Silent customer churn is no longer inevitable. It can be detected, understood, and prevented through solutions like BankChurnAI Agent.
The future of customer relationships in Haiti can be reinvented, and it begins here, within the community that decided to create rather than wait.
The message is clear: we don't need to wait for someone else to solve our problems. We have the talent, we have the ideas, and now we have proof that we can deliver concrete solutions.
The question that remains: who will be bold enough to take the step?
Interested in BankChurnAI Agent? Contact team IMPACTIS to discuss how this solution could adapt to your institution's specific needs.
Discover all hackathon projects: https://www.ayiti.ai/en/projects
About the Author:
Riché Fleurinord is captain of team IMPACTIS (with Micka Louis and Vilmarson Jules), participants at the Ayiti AI Hackathon 2025 where they developed BankChurnAI Agent, the first artificial intelligence agent dedicated to the Haitian banking sector.
