Meet your
new
Default
Prediction
Professional.
A digital credit risk professional who extracts early warning signals from behavioural, transactional, and macroeconomic data —
scoring default risk in near real time,
prioritising accounts for intervention,
and triggering tailored remediation strategies before the first payment is missed.
-The Problem
By the time a default becomes visible,
the
opportunity to prevent it has passed.
Defaults don't happen suddenly.
They are preceded by weeks or months of early warning signals —
in spending behaviour, repayment patterns, and macroeconomic conditions —
that traditional risk models, built on static inputs and periodic reviews,
are structurally unable to detect in time.
Static models use historical inputs
Legacy default prediction models are trained on historical data and score risk at origination. They don't continuously update as a borrower's circumstances evolve — so they reflect who someone was, not who they are now.
Behavioural signals go unread
Changes in repayment behaviour, spending patterns, and transactional activity often predict default months in advance — but these signals are invisible to models that don't ingest live data.
Reactive response after first missed payment
Most institutions trigger intervention only after a payment is missed. By this point, the borrower is already in distress — and the cost of recovery is significantly higher than proactive engagement would have been.
Opaque predictions undermine trust
When risk scores can't be explained, collections teams can't act on them confidently, regulators can't validate them, and customers can't be engaged meaningfully about their situation.
50%
3×
40%
30–50%
Reduction in default losses
Identification of at-risk borrowers
Early
↑High
Portfolio resilience
of broker admin time is
coordination overhead
longer quote cycles
when follow-ups
are manual
of RFQs experience at
least one missed
carrier response
A digita professional
who sees defaults
months before they happen.
The Zentis Default Predictor builds predictive models that extract rich behavioural, transactional, and macroeconomic features
to identify early warning signs of default.
Scores are computed in near real time and combined with business rules
to prioritise accounts for remediation — with explainable predictions that collections and relationship teams can act on confidently.
Audit trails are maintained for every model decision,
and integration with collections and engagement systems allows tailored intervention strategies to trigger automatically.
-The Solution
-What It Does
From live signals to
early
warning and triggered intervention.
From the moment an RFQ is raised to the moment all carrier responses
are received and compared — every step owned, every action logged.
Structures every RFQ
Creates a unique RFQ identifier, captures all required metadata, and routes to the right carriers for the coverage type — before a single email is sent.
Dispatches and tracks
Sends RFQs with embedded tracking identifiers and monitors the email channel continuously. Every carrier interaction is captured and timestamped automatically.
Classifies inbound responses
Reads and categorises every carrier reply — whether a quote, a question, an extension request, or a decline — and updates the central RFQ record instantly.
Reminds without being asked
Triggers nudges and escalations based on due dates and carrier behaviour patterns. Brokers are alerted only when a situation needs their judgment — not their time.
Surfaces comparison-ready data
Organises received quotes into a structured format ready for side-by-side comparison. No manual collation, no copy-paste from email attachments.
Maintains a full audit trail
Every RFQ action is stored securely under its unique identifier, with version control and complete logging — so every negotiation is fully reconstructable.
-Expected Impact
What changes when default
risk is
predicted, not discovered
Measurable outcomes from day one of deployment.
Reduction in default losses Early signals, early action
30–50%
Early
↑High
Identification of at-risk borrowers Months before first missed payment
Portfolio resilience Proactive remediation at scale
Regulator and stakeholder confidence Explainable, auditable predictions
↑High
Enterprise-grade by design
-Security & Compliance
Deviprasad Thrivikraman · Managing Director, Zentis AI
30+ years in global BFSI operations
30+ years in global BFSI operations
Purpose-built for regulated financial institutions with security,
governance, and explainability requirements built in from day one.
SOC 2 Certified
GDPR Compliant
BCBS 239 Ready
On-Premise Deployable
Air-Gapped Environments
LLM-Agnostic
Cloud-Agnostic
Full Audit Logging