The AI Journal speaks with Vadim Skosyrev, a strategist who has championed the shift toward data-driven reputation management, starting in one of the most demanding industries: finance.
In finance, every word moves money. Market confidence could depend on who says what, in what tone, and using which words — in a language that few outsiders fully understand. That linguistic precision made the sector a natural laboratory for predictive communication systems, where algorithms track sentiment not just by emotion, as people do, but by the vocabulary of trust and volatility itself.
From defining frameworks for predictive PR systems to leading teams that translate those frameworks into tools, Skosyrev explains how automation and human empathy can coexist — and why technology has become the new foundation of trust in modern finance.
Finance turned out to be the perfect place to rethink how reputation is built and lost. Working with financial brands, Skosyrev saw how emotions, markets, and narratives interact — and how a few hours of silence could cost millions. From that moment, he began developing the idea of predictive communications: systems that don’t just measure reputation but anticipate its shifts.
Why has reputation management become such an urgent issue for finance today?
Finance has always been about trust — an invisible contract between institutions and people. Over the last decade, the speed of information has completely changed the rules. Reputation no longer erodes through scandals alone; it can be chipped away by micro-events that start as comments in local Facebook groups or forwarded Telegram posts.
Money moves faster than narratives, and when confidence drops, people act immediately. In this environment, PR can’t afford to be only reactive. It had to evolve into PR-tech tools capable of interpreting early data signals. Communications must evolve from intuition to infrastructure.
How do early-warning systems in reputation management actually work under the hood?
At a high level, these systems collect public-domain data — from social networks, messenger channels, forums, and news feeds — and pass it through several analytical layers. First comes ingestion: raw text and metadata flow into a pipeline that normalises, cleans, and timestamps every piece of content. Then anomaly-detection models — think of ARIMA or Isolation Forest — monitor the usual rhythm of mentions and sentiment around a brand. When that rhythm breaks, the system looks deeper: Is it a spike or a structural shift?
The key is to define the logic behind what counts as a signal and what doesn’t. The challenge is interpretation: not every spike is an event. Each organisation needs its own baseline of “normal volatility.” Once deviations exceed that threshold, alerts are routed to communication teams who decide whether to intervene.
I prefer to avoid the loud phrasing like “predicting scandals.” What we’re really doing is recognising signal patterns — subtle shifts that quietly reshape perception before they become visible to everyone else.
Why is the financial sector susceptible to public sentiment?
Because financial reputation is measurable in money. When a food brand faces criticism, sales might fall for months. When a bank loses trust, it can face a liquidity problem within hours. Every statement, every delay, every rumour translates into client behaviour.
The regulatory environment amplifies that sensitivity. Financial institutions must demonstrate stability and integrity — not just performance — to clients and regulators alike. Even a hint of malpractice or lack of transparency can trigger chain reactions: investors withdraw, journalists investigate, and clients panic.
One case demonstrated how early detection of a growing wave of complaints about a savings product enabled a client to act before the issue reached national media. Under normal circumstances, the team would have taken hours to validate the situation and align their message. In this case, they reacted within minutes. If they had stayed silent, by morning, they would have faced a storm of headlines and social outrage. But when you can detect what threatens you early, you’re no longer paralysed by uncertainty — you act.
The company didn’t silence the discussion but addressed it transparently, clarified terms, and retained most customers. That experience shaped a conviction that predictive systems will define the future.
How does the algorithm distinguish short-term noise from a real trend that requires human attention?
We deal with information noise every day — and that’s where early-warning systems show their real value. A few heated comments in a Telegram thread might look alarming, but they don’t necessarily mean the story will grow. The system evaluates three key dimensions: duration, diversity, and context.
Duration measures whether the conversation persists beyond a few hours. Diversity looks at how many distinct sources pick it up — if it stays within one group, it’s not a crisis. Context is linguistic: we track if emotionally charged keywords like “breach,” “regulator,” “fraud,” or “lawsuit” start appearing together. When all three align, the system escalates it to human review.
Overreacting to noise damages credibility; missing a slow-moving trend can cost millions. These tools help communication teams decide the right level of involvement and timing for response.
What makes language modelling in finance so challenging?
Financial language is counterintuitive. Words that sound negative elsewhere can be neutral or even positive in finance. “Short position” or “interest rate cuts” might signal risk to a generic model, but an opportunity to an investor. Financial communication also carries layers of irony that even advanced models struggle to decode.
Sarcasm in finance is not humour — it’s a shorthand for scepticism. An analyst might tweet “excellent results, as usual” after a company misses earnings, or investors might joke about “strong fundamentals” when the market clearly disagrees. To a neural model, those lines sound optimistic; to a strategist, they signal eroding trust. When systems learn to capture this, sarcasm acts like an early marker of mood change. It appears before numbers shift, before volatility spikes.
Once public language starts diverging from professional tone, sentiment is already drifting. The challenge is cultural as well as linguistic — sarcasm in American finance looks different from British, Russian, or Arabic business discourse. Models must combine statistical cues (word order, punctuation, emoji) with semantic context — essentially teaching algorithms when irony replaces anger, and when it hides it.
For communications, this becomes a map of emotion in real time. It shows not just what people say, but how far their tone is from what they used to mean by those exact words.
That’s why domain-specific models like FinBERT and aspect-based sentiment analysis (ABSA) matter. They evaluate sentiment by aspect — product, leadership, ethics — and detect nuances like sarcasm or irony. The communication logic that such models must capture involves tone, emotional distance, and ethical framing.
The modern era of these models began with the widely known 2017 paper “Attention Is All You Need.” But domain adaptation still depends on people who understand both language and behaviour. Technology can learn the patterns, but it needs experienced strategists to define what they mean within a particular domain.
How do data signals translate into actual decisions?
The bridge between data and action is context. The system aggregates signals — sentiment shifts, topic frequency, anomaly duration — and correlates them with past outcomes. When correlations cross a threshold, the event enters a triage dashboard.
From there, communication teams review the signals and decide on a strategy: acknowledgement, clarification, or silence. In one case, automation detected a risk cluster twelve hours before it reached journalists. The brand prepared a transparent statement and contained the issue within a day. Without that time advantage, the story would have turned into a national headline.
Automation doesn’t replace judgment; it removes friction. It shortens approval chains and allows people to focus on meaning, not monitoring.
How do you measure whether such a system works?
Teams usually evaluate three things: accuracy, speed, and impact. Accuracy — often above 90 per cent — ensures that alerts are meaningful. F1-score, around 0.89, balances precision and recall, meaning the system is both selective and attentive. False positives — unnecessary alarms — should stay below 10 per cent.
But numbers matter only if they change behaviour. When reaction time drops from days to minutes and negative coverage decreases by 80 per cent, that’s transformation. These aren’t just analytics — they’re operational shifts that redefine how trust is managed.
What part of this process still depends on people?
AI can process data, but it can’t sense emotion. It can identify irony but not its social meaning. Humans interpret, contextualise, and act with empathy.
When teams see objective data on what audiences actually discuss, their decisions become faster and more grounded. Hierarchies shrink because everyone sees the same facts. AI doesn’t replace communicators — it gives them clarity. And clarity builds confidence.
How do you see these technologies becoming accessible to smaller teams?
Until recently, only large institutions could afford this level of analysis. But modular cloud services have changed that. Today, any team can connect social APIs, define key entities, and receive alerts in Slack or CRM. What used to take months of engineering now takes days.
This accessibility matters. Reputation resilience shouldn’t depend on budget size. For small fintech startups, early-warning systems act like sensors for trust — early indicators that allow them to stay transparent and agile in a crowded market.
Looking ahead — where is PR-tech going next?
The next wave will be multimodal and explainable. Systems will analyse not just text but tone, visuals, and behavioural cues. The priority will shift from detection to understanding: why something resonates so emotionally, making people shout, and how credibility can be rebuilt faster than just waiting till everyone forgets your fault.
AI will help quantify trust, but ethics and transparency will define its limits.
Over the past decade, this data-driven mindset has started to reshape corporate culture itself. Teams that once relied on intuition now measure trust with dashboards; editorial decisions become experiments in causality. We used to ask whether the story felt right; now we ask what reaction pattern it fits into.
The important thing is that AI shouldn’t make communications soulless. The next challenge is not to collect more data, but to teach organisations to interpret it ethically — to understand the human story behind the metrics.
That’s why the most valuable skill in modern PR is the ability to move between disciplines: data science, behavioural psychology, and storytelling. The strategist of the future is someone who understands how people and systems think — and who can make them talk to each other.
Reputation is now a measurable asset, but it remains deeply human. The tools built to protect it are becoming more sophisticated, yet their purpose is simple: to help people act with clarity before panic takes over.
Technology doesn’t make communication less human — it gives visionaries the frameworks to design trust itself.
