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VT Risk was founded by practitioners who built risk and collection departments for banks and IFNs before building the software. Our AI/ML products move the numbers that appear on the balance sheet — cost of risk, operational cost, personnel cost — by predicting client behaviour and automating the decision that follows.
A rare combination of executive strategy and deep technical expertise — built at the intersection of finance, technology and behavioural data.
Founder & CEO, VT Risk
Two decades building risk, collection and decision systems for financial institutions — then turning that insider knowledge into AI/ML products that move the operating numbers, not just the dashboards.
View LinkedIn ProfileExecutive MBA graduate. ASEBUSS runs its International EMBA in academic partnership with Kennesaw State University (KSU), Atlanta, Georgia, USA.
As an ASEBUSS alumnus, invited to design and teach the first Digital Transformation course delivered inside an EMBA program in Romania.
20+ years delivering data, risk and automation projects across multinational telecom and financial-services groups in EU & CEE markets.
Constructed 6–7 collection and risk departments from zero for top-tier Romanian banks and IFNs before founding VT Risk.
Featured in Ziarul Financiar — Romania's leading business daily — on debt-recovery markets, non-payment risk and the data behind the decisions.
Vlad Brătășanu on how statistical and mathematical tools, applied to client behaviour, turn receivables management into a measurable, automated process.
A market-readout discussion on non-payment risk trends and the behavioural traits that signal distress before a default lands on the balance sheet.
Beyond consulting, VT Risk publishes a dedicated magazine on non-performing loan management, behavioural risk and operational recovery — read by banking, IFN and collections professionals across the region.
Measurable outcomes from inside the institutions that shaped the market.
20+
Years in Market
Risk, data & automation for finance
15+
Enterprise Clients
Banks, IFNs, telecom & leasing
6–7
Departments Built
Collection & risk, from zero
€47M+
Receivables Recovered
Across managed portfolios
+20%
Recovery Uplift
AI/ML vs. traditional methods
>85%
Score Accuracy
Predictive recoverability models
A score is only valuable if it changes an action. Every model we ship is wired into an automated decision — contact, limit, segment, route — by construction, not by committee.
The same client-behaviour score flows unchanged across origination, monitoring, retention and collections — eliminating the data drift that breaks most analytics stacks.
BNR, GDPR and consumer-protection rules are built into every workflow from day one. Depersonalised modelling, audit trails and explainable decisions — no retrofitting.
We deploy against your live data and report against your KPIs. Uplift is measured in the operating numbers, not in a slide deck.
VT Risk began with a simple observation: the people who understand credit risk and collections best — the ones who sat on the bank's side of the table — almost never build the software that runs them. The result is a market full of generic tools that describe risk after it has already become a loss.
Our founder, Vlad Brătășanu, spent two decades doing the opposite. He built six to seven collection and risk departments from zero for institutions like Patria Bank, BRD, Banca Transilvania, GE Money, Motoractive and Estima Finance. He designed the policies, recruited the teams, selected the vendors — and then decided to become the best vendor available.
That insider knowledge is the foundation of every VT Risk product. We don't ship a dashboard and leave. We ship a behavioural model wired into an automated decision, we deploy it against your live data, and we report against the KPI your CFO actually reads. The uplift shows up in the operating numbers — from the first month.
Predictive models and automation across the full customer lifecycle — all driven by client behaviour, all measured against your operating KPIs.
Debt-collection, SME and consumer scorecards that predict payment, default and recovery propensity.
Propensity-to-leave models and contact-strategy engines that protect revenue before it walks.
Decision trees that route each case to the right channel, moment and treatment — automatically.
Acquisition risk and credit-limit models that grow revenue without growing the cost of risk.
Whether you're evaluating a vendor, scoping a pilot, or an investor assessing the opportunity — we'll walk you through the models, the deployment and the measurable impact in 30 minutes.