Looking for a more specific outcome? We’ll build a solution to get you there.
Looking for a more specific outcome? We’ll build a solution to get you there.
RBM Software helps BFSI enterprises modernize these foundations so intelligence becomes the execution layer, not an added plugin. Our approach replaces document-heavy, sequential workflows with data-driven automation that brings accuracy, speed, and auditability to every financial process.
RBM integrates consulting, cloud-native platform engineering, applied AI, and domain-aligned operating models, enabling BFSI & insurance enterprises to unlock financial intelligence across risk, fraud, onboarding, servicing, claims, collections, and regulatory processes. We don’t just add tools. We re-architect workflows around predictive models, policy controls, identity verification automation, behavioral insight, and compliance-first data pipelines.
We embed ML-based anomaly detection inside transactional and behavioral flows, observing micro-signals across device, location, merchant networks, spending rhythm, outlier velocity, card usage, and identity-linked data. Instead of rule triggers, models continuously refine pattern understanding, enabling proactive fraud blocking, not reactive intervention.
Key Application: real-time fraud interception across card, wallet, UPI, ACH, RTGS, and payment rails.
We enable AI-first conversational orchestration that eliminates wait times, manual call center queues, and repetitive inquiry workloads. Intents are resolved instantly across claim status, EMI queries, policy eligibility, balance clarification, dispute explanation, and product qualification.
Key Application: multilingual financial service resolution with human escalation only when required.
We build personalization models that convert the full signal graph, spending patterns, category behavior, wealth markers, product mix, repayment behavior, and intent events into individualized financial recommendations.
Key Application: next-best-offer / next-best-action engines for contextual cross-sell that increase product penetration, without increasing noise.
We build model-driven risk scoring engines that combine bureau, behavioral, and alternative signals to enable fairer and faster lending decisions. Traditional underwriting often limits credit access because information scarcity penalizes thin-file users, and our approach helps overcome that barrier.
Key Application: ML credit scoring for instant approvals in cards, BNPL, personal loans, SME loans, and embedded finance.
We transform collections into a proactive, behavior-led engagement journey rather than a post-default pursuit. AI dynamically negotiates payment plans based on user profiles, repayment probability, tone preference, past interactions, and delinquency stage.
Key Application: automated multilingual NPA nudging to reduce default runway and accelerate settlement.
We turn claims from document routing to automated evidence understanding. AI extracts data from forms, medical papers, images, invoices, reports, cross-checks against coverage rules and historically observed outcomes, then prioritizes or auto-clears low-risk claims.
Key Application: automated claims triage and auto-adjudication across motor, health, life & general insurance.
Frontline onboarding time kills conversion. We automate identity extraction, document parsing, field mapping, AML watchlist checks, and sanction scans, compressing days into minutes.
Key Application: fully digital e-KYC with instant verifications and compliant identity affirmation.
Partner with RBM to engineer the next generation of digital, secure, and experience-led financial ecosystems.
RBM uses decision engines, secure cloud and data pipelines to improve underwriting fraud, routing claims, and service journeys with clear measurable gains.
| Technologies | What We Do | Business Outcome |
|---|---|---|
| ML fraud detection, anomaly models, NLP | Prevent fraud and accelerate triage | Reduced loss ratio & false positives |
| e-KYC APIs, OCR / NLP | Automate identity & onboarding | Higher conversion, lower verification cost |
| LLM + voice + agent automation | Scale support resolution without headcount | Lower OPEX, higher CSAT |
| Predictive risk scoring | Real-time credit underwriting | Increased approval rates with stable default ratios |
| AI claims intelligence | Touchless adjudication flows | Claims cycle compression & accuracy lift |
| Data pipelines, feature stores | Enterprise-wide signal availability | Single version of truth for decision automation |
| DevSecOps, Zero Trust cloud infra | Secure-by-design deployment | Compliance-first modernization |
Practical, future-ready transformation opportunities across the BFSI and insurance value chain.
Financial services risk posture can no longer rely on rules. Fraud evolves faster than rules can update. AI learns spending signatures, behavioral fingerprints, device identity, merchant networks, location anomalies, and velocity patterns to detect emerging fraud before theft occurs. Instead of cleaning damage, institutions prevent loss. Fraud teams shift from case chasing to proactive mitigation. Risk posture becomes dynamic, not static, reducing false positives, reducing manual investigations, lowering losses, and protecting brand trust, without slowing customer transactions.
Contact centers waste enterprise OPEX through repetitive queries. AI agents eliminate call queues and resolve straightforward intents instantly. Multilingual capability solves cross-regional barriers. Humans are reserved for escalations, not FAQs. Customers get clarity, speed, and resolution, not transfers. Institutions reduce ticket load, call handle time, and operational cost, while increasing first-contact resolution and CSAT.
This improves service economics and strengthens lifetime customer relationship quality.
Generic outbound campaigns lead to low conversion and wasted marketing budget. AI product engines model next-best-product opportunity based on spend type, risk posture, seasonal behavior, lifecycle stage, income rhythm, and product holding patterns. Financial products are no longer “sold”; they are contextually offered based on predicted fit and timing. Revenue uplift becomes continuous, not campaign-based, and customers perceive advisory, not selling.
Traditional underwriting excludes capability because traditional underwriting lacks dimensional insight. AI risk scoring integrates bureau + transactional + behavioral + alternative data into real-time lending logic. Thin-file users become eligible, underserved users become issuable, decisions become fairer, and approval volume increases without increasing loss. Risk becomes probabilistic, adaptive, and explainable, not static and opaque.
Manual calling scripts fail because humans vary in tone, approach, language, persistence, and context. AI automates multilingual negotiation based on debtor persona and repayment likelihood, escalating offers, suggesting payment plans, or routing to human negotiation when the probability is high. NPA remediation becomes conversion science, not manpower intensity. This reduces default impact, increases settlement velocity, and protects portfolio health while lowering collections cost.
Insurance claims involve a lot of data, but they do not require much judgment. Models take in medical files, pictures, invoices, damage reports, adjuster notes, extract values, check rules, estimate the probability of the claim being legitimate, and decide on the payouts that need to be made first. Operational overhead collapses. Fraud leakage shrinks. Decisions become objective, explainable, and consistent. Payout speed becomes a brand differentiator, not a cost center.
Document-driven queues slow conversion. By using OCR + NLP, the entire process of value extraction is done automatically, fields are standardized, identity is verified, matched with sanction lists, and onboarding approval or risk review is triggered within a few seconds. KYC doesn’t slow down the process anymore; instead, it is transformed into a direct workflow.
As a result, institutions can get new customers quicker, the records for audits are more transparent, and the risk of non-compliance is reduced.
Let’s co-engineer the next generation of secure and compliant financial platforms.
Transforming the retail business is a huge task that cannot be handled by one single platform. A whole ecosystem that is tailored for agility, personalization, and speed is required.
RBM brings together a unified stack of up-to-date technologies that link stores, data, and infrastructure to make it possible for retailers to innovate at a faster pace, personalize customer experiences, and expand their business without worries.
React.js
NEXT.js
Angular
Flutter
React Native
Node.js
GO
.NET Core
Spring Boot
AWS
Microsoft Azure
GCP
Terraform
Kubernetes
Databricks
Snowflake
Kafka
Airflow
TensorFlow
PyTorch
OpenAI APIs
Scikit-learn
UiPath
Power Automate
Automation Anywhere
Jenkins
GitHub Actions
Prometheus
Grafana
Splunk
Qualys
Vault
Prisma Cloud
Salesforce
HubSpot
Adobe Experience Cloud
A consultative engineering model that transforms BFSI & insurance enterprises from rules-driven operations into model-driven financial intelligence, reducing risk exposure, accelerating decisions, and enabling compliant AI at scale.
Leverage AI, data engineering, and real-time analytics to minimize risk exposure, enhance forecasting accuracy, and enable intelligent decision-making across your financial enterprise.
Our custom eCommerce application development services assist you in re-inventing your business by silo busting. We leverage partnerships with top technology vendors to offer you integrated platforms and eCommerce solutions development with advanced, industry-specific functionalities that unlock new revenue streams.
MongoDB
At RBM, transformation is not an automation sprint; it is a value-operating shift. We engineer BFSI systems where underwriting, onboarding, servicing, collections, fraud prevention, and claims are no longer manually administered; they are continuously optimized through data, models, and policy-driven controls. Our approach is advisory-first, architecture-centric, security-anchored, and built for measurable impact, not proof-of-concept theater.
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We design platforms where every workflow, onboarding, lending, claims, fraud, and policy servicing is model-augmented. Systems do not just store information; they infer patterns.
Benefits:
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We implement models with auditability, transparency, and policy guardrails built in, not bolted on. Every score is traceable, every approval is explainable, and every action is governed by compliance envelopes aligned to RBI, IRDAI, PCI DSS, and ISO protocols.
Benefits:
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We design Zero Trust, tokenized, cloud-agnostic architectures that can run on AWS, Azure, GCP, or hybrid, without compromising data residency mandates or threat posture.
Benefits:
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We believe transformation doesn’t end at go-live. Models drift, behaviors change, and fraud patterns evolve. RBM operates through continuous MLOps, metric-based iteration, and quarterly innovation cycles, ensuring systems never stagnate.
Benefits:
Empower your enterprise with AI-driven decision systems, cloud scalability, and predictive data intelligence that reduce risk, accelerate decisions, and enable future-ready financial performance.
Rule engines stop only at what they already know. Fraud patterns mutate daily. AI fraud intelligence learns from behavioral signals, velocity anomalies, merchant graph changes, device fingerprint shifts, and transactional context. This enables early detection before loss, not forensic cleanup after an attack. AI also reduces false positives and manual review overhead, enabling fewer escalations, stronger trust, and materially better fraud loss ratios.
We use feature stores, model governance, explainability frameworks (SHAP/LIME), and policy-based controls so decisions remain traceable, not black-box. Every approval/rejection path can be audited. Regulators care about logic traceability, not just accuracy — so we deploy scorecards with reasoning surfaces, not opaque neural outputs. This ensures risk models remain transparent, audit-ready, and aligned to RBI / IRDAI-controlled environments.
Yes. Compliance risk actually reduces with automated KYC because OCR + NLP eliminate manual errors, mis-keyed fields, and inconsistent human judgment. AML/sanction checks run in parallel, automatically, not sequentially. Automated workflows produce complete, structured evidence, not paper trails, which makes audits cleaner. Conversion increases because onboarding time collapses, while compliance consistency improves, not weakens.
Most claims aren’t subjective; they’re repetitive pattern matching. AI extracts structured fields from documents, medical papers, images, and loss reports, then cross-validates coverage rules, exclusions, and historical patterns. Human adjusters are still used for ambiguity, disputes, or fraud suspicion. The goal is triage automation, not replacing actuaries. This reduces claims TAT, improves accuracy, and differentiates CX.
Traditional campaigns spam entire segments. AI personalizes offer timing and context, predicting which product matches which behavior profile and life stage moment. This reduces marketing wastage and lifts conversion quality. Cross-sell becomes advisory, not promotional noise. It increases revenue efficiency, not just revenue volume.
Collections automation shifts recovery from brute force human calling to behavioral negotiation intelligence. Institutions usually see higher conversion rates (because offers are tuned to likelihood models), reduced collection costs (because fewer manual cycles happen), and less NPA drag. Humans stay focused on high-value, difficult accounts, while automation handles 80% of volume.
We deploy fairness audits, bias detection, balanced training sets, challenger models, and human-in-loop override logic. Regulatory frameworks care about explainability, not just accuracy. AI underwriting does not remove accountability; it removes blind guesswork. Final decisions remain accountable, traceable, and bias-governed.
Yes, when engineered with Zero Trust, tokenization, encryption-at-rest/-in-flight, identity federation, workload isolation, and DevSecOps guardrails. The problem isn’t cloud; the problem is unmanaged cloud. We establish compliance-first multi-cloud architectures aligned with RBI, PCI DSS, ISO 27001, and data residency constraints. The result is more security, not less.
AI decisioning layers can be deployed around the core, not instead of it. We implement domain microservices that wrap heavily coupled legacy functions. This allows transformation to be incremental, not disruptive. Enterprises don’t have to “rip” the core; they can surround it with intelligence.
Partner with us to build intelligent, secure, and compliant ecosystems that transform banking, insurance, and financial operations, driving efficiency, personalized customer experiences, and measurable business impact.
