Unified customer intelligence, AI-powered credit risk, fraud detection and lending optimisation for modern banking operations.
Unified customer profiles with household mapping, CLV prediction, churn detection, and next-best-action for banking.
Banks maintain customer data across multiple disconnected systems — Core Banking, Salesforce FSC, Loan systems, and AML. This fragmentation creates:
Relationship managers cannot see a customer's full portfolio (deposits + loans + cards + investments + advisory history) in one place
Linked family members, joint accounts, and corporate-individual relationships are not mapped, leading to fragmented servicing
High-net-worth customers reduce balances and shift products to competitors over months without triggering any alert
Customers with large savings but no investment relationship are never flagged despite high conversion propensity
Different channels (branch, digital, call center) operate on different data, creating contradictory experiences
Single golden record combining Core Banking (accounts, balances, transactions), Salesforce FSC (interactions, pipeline, advisor notes), Loan Origination (borrowing history, repayment behavior), and AML (risk flags, KYC status).
Primary
Spouse
Joint account
Business owner
Dependant
Household · 5 membersAutomatically discover and map household relationships (spouse, children, parents), corporate affiliations, and beneficial ownership structures. Calculate household-level AUM and total relationship value.
ML model predicting 3-year forward revenue per customer based on product holding trajectory, balance trends, transaction patterns, and life-stage indicators.
Detect early signals of customer attrition: declining balances, reduced transaction frequency, competitive product inquiries, and negative sentiment from service interactions.
Context-aware recommendations for each customer interaction: product offers, service recovery actions, relationship deepening suggestions, and compliance requirements.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Customer / CIF | CBS Customer Master + Salesforce Account | CIF Number, Name, DOB, Segment, KYC Status |
| Accounts & Balances | CBS + Loan Origination | Account Type, Balance, Currency, Status, Interest Rate |
| Transaction History | CBS Ledger | Date, Amount, Type, Channel, Counterparty, Category |
| Interactions & Advisory | Salesforce FSC Activities | Date, Channel, Type, Advisor, Notes, Outcome |
| Risk & Compliance | AML System + CBS | KYC Status, Risk Rating, AML Alerts, PEP Flag |
Match customer identity across CBS CIF, Salesforce Account, LOS Borrower, and AML subject into a single golden record using probabilistic matching
Graph-based analysis of joint accounts, shared addresses, common beneficiaries, and Salesforce relationship groups to build household structures
Aggregate all product holdings, balances, transaction patterns, interaction history, loan performance, and risk flags into a unified profile
Gradient-boosted model trained on historical customer revenue trajectories with features including product depth, balance trend, and tenure
Survival analysis model identifying customers with elevated exit probability in the next 90 days using balance velocity and competitive signals
Contextual recommendation engine combining CLV, product holding gaps, life-stage triggers, and compliance requirements
Customer 360 dashboard for relationship managers; churn alerts pushed to Salesforce; NBA recommendations surfaced in branch/call center systems.
Sum of all product balances + off-balance-sheet AUM
Avg active product categories per customer
Annual attrition rate for high-net-worth segment
Predicted vs. actual 12-month revenue correlation
% of recommendations resulting in product sale
% of customers linked to a household group
TRV > $500K with churn probability > 0.65
Balance declines >25% in 60 days
KYC documents expire within 30 days
High-CLV customer with deposit but no investment
New account in existing household
Annually from retained AUM, increased product penetration, and RM productivity — across a mid-size bank with $5B in customer assets under management.
Customer entity resolution across CBS and Salesforce FSC is the critical foundation; expect 85-92% auto-match rates with manual review for remaining ambiguous records
Household discovery requires joint account data and shared address/phone analysis; accuracy improves with Salesforce relationship group data
CLV model needs minimum 24 months of product and balance history for training
Next-best-action engine should integrate with the bank's existing campaign management platform to avoid duplicate outreach
AML risk flags must be surfaced in the profile but access-controlled to authorized users only
AI-powered credit scoring, portfolio monitoring, and expected credit loss calculation for proactive risk management.
Banks rely on credit bureau scores and static risk ratings that provide a point-in-time snapshot but fail to capture emerging risk. This leads to:
Deterioration signals (declining balances, industry stress, behavioral changes) are invisible until missed payments occur
Customers with identical bureau scores have vastly different actual risk profiles based on transaction behavior and relationship depth
IFRS 9 Expected Credit Loss (ECL) models rely on stale inputs, leading to over- or under-provisioning that impacts capital efficiency
Credit teams spend weeks on quarterly reviews that could surface issues in real time
Sector, geography, and single-name concentration builds undetected until stress events hit
Augment bureau scores with behavioral data: transaction patterns, balance trends, account utilization, and relationship depth — producing a more predictive composite risk score.
Continuous monitoring of leading indicators: payment pattern deterioration, deposit balance erosion, sector stress signals from market data, and adverse news on borrowers.
Automated Expected Credit Loss calculation with AI-driven PD, LGD, and EAD models. Stage classification (1/2/3) based on significant increase in credit risk (SICR) triggers.
Real-time portfolio-level dashboards showing concentration risk by sector, geography, product, rating grade, and vintage — with drill-down to individual exposures.
Model portfolio performance under macroeconomic stress scenarios (rate hikes, GDP contraction, sector downturns) to assess capital adequacy and provisioning impact.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Borrower Profile | LOS + CBS + Documents | Borrower ID, Name, Segment, Bureau Score, Income, Employment, Financials |
| Loan / Exposure | Loan Origination System | Loan ID, Product, Sanctioned Amount, Outstanding, Rate, Tenor, Status |
| Repayment History | Core Banking System | Due Date, Paid Date, Amount Due, Amount Paid, DPD (Days Past Due) |
| Collateral | Loan Origination System | Collateral Type, Valuation, LTV Ratio, Last Valuation Date, Lien Status |
| Macro Indicators | Market Data (Bloomberg/Refinitiv) | GDP Growth, Unemployment Rate, Interest Rate, Sector Indices, CPI |
Combine bureau scores with CBS behavioral features (transaction velocity, balance trends, account utilization, relationship tenure) and LOS loan performance data
Train gradient-boosted model on historical default events; produce a composite risk score that outperforms bureau-only scores by incorporating behavioral signals
Continuous scan of leading indicators: DPD trends, deposit balance decline in linked accounts, sector index deterioration, adverse news mentions, collateral value movement
Evaluate whether each exposure has experienced a Significant Increase in Credit Risk since origination; determine IFRS 9 stage migration (Stage 1 → 2 → 3)
Compute 12-month ECL (Stage 1) and lifetime ECL (Stage 2/3) using AI-driven PD term structures, LGD models incorporating collateral recovery, and EAD models
Roll up exposure-level risk metrics into portfolio views by segment, sector, geography, vintage, and rating grade
Risk dashboards for credit committee; EWS alerts to relationship managers; ECL numbers to finance for provisioning; stress test results to ALCO.
Non-performing assets / Gross advances
Total provisions / Gross NPAs
Total ECL provision / Total loan book
% of defaults flagged by EWS at least 90 days prior
% of Stage 1 exposures migrating to Stage 2 per quarter
% of total exposure in top 10 borrowers
Borrower triggers 3+ early warning indicators simultaneously
Loan crosses 30 DPD for the first time
Exposure migrates from Stage 1 to Stage 2
Collateral market value drops >20% since last valuation
Industry sector index declines >15% in 90 days
Annually from reduced NPAs, optimized provisioning, earlier intervention, and capital efficiency — across a mid-size bank with a $2B loan portfolio.
Requires minimum 3-5 years of loan performance history (origination to resolution) from the Loan Origination System for PD model training
CBS transaction data is critical for behavioral scoring; daily account-level balances and transaction feeds must be available
IFRS 9 ECL model outputs must be validated by the bank's model risk management team and approved by auditors before production use
Stress testing scenarios should align with regulatory guidance (central bank prescribed scenarios + internal scenarios)
Market data feeds for sector indices and macro indicators require Bloomberg/Refinitiv API integration
Real-time transaction anomaly detection, network analysis, and automated suspicious activity reporting.
Banks face an escalating volume and sophistication of financial crime. Existing rule-based AML systems generate massive alert volumes with low true-positive rates:
AML teams process thousands of alerts monthly, 95%+ of which are false positives, draining investigator capacity
Static rules catch known patterns but miss novel fraud typologies and evolving laundering techniques
Card fraud, digital fraud, and AML operate as separate systems with no correlation across crime types
Investigators manually assemble evidence from multiple systems, taking 4-8 hours per case
Suspicious Activity Reports are manually drafted with inconsistent quality and narrative depth
Money laundering rings involve interconnected entities that rule-based systems evaluate in isolation
ML-based scoring of every transaction against the customer's behavioral baseline — flagging statistical outliers in amount, frequency, geography, counterparty, and channel combinations.
Graph-based detection of money laundering networks: identify clusters of related accounts, circular fund flows, rapid layering patterns, and hidden beneficial ownership structures.
AI model trained on historical alert dispositions to auto-suppress low-risk alerts and prioritize genuine suspicious activity, reducing investigator workload by 40-60%.
Automatically compile a comprehensive case package: customer profile, transaction timeline, linked entities, KYC documents, prior alerts/SARs, and risk indicators.
LLM-powered drafting of Suspicious Activity Report narratives based on assembled case evidence, with investigator review and approval workflow.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Customer / Entity | CBS + AML System | CIF, Name, Type (Individual/Corporate), Risk Rating, Country, PEP Status |
| Transactions | Core Banking System | Txn ID, Date, Amount, Currency, Type, Channel, Originator, Beneficiary |
| AML Alerts | Transaction Monitoring (NICE Actimize/SAS) | Alert ID, Rule, Score, Status, Assigned Analyst, Disposition, Date |
| KYC Documents | Document Management (SharePoint/Box) | Document Type, Customer, Verification Status, Expiry, Source |
| Sanctions & PEP Lists | Market Data + Regulatory Feeds | List Source, Entity Name, Match Score, List Date, Jurisdiction |
Build per-customer transaction profiles: typical amounts, frequency, counterparties, channels, and geographic patterns from CBS historical data
Score each incoming transaction against the behavioral baseline using isolation forest + autoencoder ensemble; flag deviations beyond configured thresholds
Build a transaction graph connecting accounts via fund flows; apply community detection and cycle detection algorithms to identify suspicious clusters
Apply a classification model trained on historical alert dispositions to score and prioritize new alerts
For prioritized alerts, automatically retrieve: customer 360 profile, 90-day transaction timeline, linked entities, KYC status, prior alerts/SARs, and sanctions matches
LLM generates a narrative SAR filing based on the case package, structured per regulatory requirements, with investigator reviewing and approving
Alert queue dashboard with AI-prioritized ranking; case packages for investigators; SAR drafts for review; network visualization for complex cases.
% of alerts that result in genuine suspicious activity finding
% of alerts dismissed as non-suspicious
Number of SARs filed per 100 alerts
Hours from alert to disposition
% of SARs filed within regulatory deadline
% of laundering cases where network analysis identified additional linked entities
Transaction anomaly score > 95th percentile + high-risk customer
New circular fund flow pattern detected across 3+ accounts
Customer or counterparty matches sanctions/PEP list with score > 85%
Customer transaction count exceeds 5x monthly average in 24 hours
Account inactive >12 months suddenly receives large inbound transfer
Annually from reduced investigation costs, lower fraud losses, avoided regulatory fines, and improved detection rates — across a mid-size bank processing 5M+ transactions monthly.
Real-time transaction scoring requires low-latency access to CBS transaction feeds (sub-second for card transactions, near-real-time for wire/ACH)
Network analysis is computationally intensive; graph database (Neo4j or Amazon Neptune) recommended for relationship queries
SAR auto-generation requires LLM fine-tuning on historical SAR narratives; the bank's compliance team must approve the template and review workflow
Model training on historical alert dispositions requires clean labeled data — expect a 3-4 month disposition history cleanup effort
Sanctions list integration requires daily updates from OFAC, EU, UN, and local regulatory lists
Origination funnel optimization, collections prioritization, NPA prediction, and collateral monitoring for the lending business.
The lending lifecycle — from application to disbursement to repayment to recovery — spans multiple systems and teams. Inefficiencies at each stage compound into significant revenue loss and risk exposure:
Loan applications take 7-15 days from submission to disbursement due to manual document verification, underwriting bottlenecks, and approval chains
30-40% of qualified applicants abandon the process due to long turnaround times, losing the deal to competitors
Delinquent accounts are contacted using static aging buckets rather than risk-based prioritization, wasting effort on accounts that would self-cure
Loans transition to non-performing status without sufficient advance warning for proactive restructuring
Property and asset collateral values are updated annually or on-event, not monitored continuously against market movements
Loan files contain dozens of unstructured documents requiring manual verification
End-to-end visibility into the loan application pipeline: conversion rates at each stage, bottleneck identification, and TAT analysis.
AI extraction and verification of data from loan documents: income proofs, bank statements, title deeds, financial statements — reducing manual processing time by 70%.
ML-based scoring of delinquent accounts by recovery probability, amount at risk, and optimal contact strategy — ensuring the collections team focuses effort where it yields the highest recovery.
Early identification of accounts likely to transition to NPA status within the next 90 days, enabling proactive restructuring offers before the account deteriorates beyond recovery.
Continuous monitoring of collateral values (real estate, vehicles, securities, gold) against market indices, triggering re-valuation and LTV recalculation when movements exceed thresholds.
Source systems mapped into the NexusAI ontology, reasoned over, and turned into the KPIs your teams track.
NexusAI ontology · reasoning · agents
| Ontology entity | Source system | Key fields |
|---|---|---|
| Loan Applications | Loan Origination System | Application ID, Applicant, Product, Amount, Stage, Submit Date, Decision |
| Active Loans | LOS + CBS | Loan ID, Product, Outstanding, EMI, Rate, Tenor, DPD, Bucket, Status |
| Repayment Behavior | Core Banking System | Due Date, Paid Date, Amount Due, Amount Paid, Payment Mode, Bounce Count |
| Loan Documents | Document Management (SharePoint/Box) | Doc Type, Applicant, Verification Status, Extracted Data, Upload Date |
| Collateral Values | LOS + Market Data | Collateral Type, Original Value, Current Market Value, LTV, Last Valuation Date |
Track every loan application through each origination stage with timestamps; calculate stage-wise conversion rates and identify bottlenecks
LLM + OCR pipeline to extract structured data from loan documents: income figures, employer details, property addresses, financial ratios; validate against application data
Feed extracted document data into credit scoring models alongside bureau data and CBS behavioral features; generate underwriting recommendation
For delinquent accounts (1+ DPD), run a propensity model predicting: probability of self-cure, probability of recovery with contact, optimal contact channel, and best time
Flag accounts with 60%+ probability of reaching 90 DPD within the next 90 days based on repayment trajectory, balance trends, and borrower behavioral signals
Monitor property indices, gold prices, security market values against loan collateral; trigger automatic revaluation when market movement exceeds ±10%
Origination pipeline dashboard for business heads; collections work queue for recovery team; NPA watchlist for credit team; collateral alerts for risk team.
Average days from application to disbursement
% of applications reaching disbursement
Average hours per loan file (all documents)
% of 30+ DPD accounts resolved within 60 days
% of new NPAs that were flagged 90 days prior
% of collateralized loans with current market valuations
Account predicted to reach 90 DPD with probability > 0.7
Stage-wise TAT exceeds 2x benchmark for 3 consecutive days
Collateral market value decline pushes LTV above 85%
Document AI confidence score < 60% on income proof or ID
Delinquent account not contacted within 5 days of entering 30+ DPD
Annually from faster origination, reduced abandonment, lower processing costs, improved recovery, and proactive NPA management — across a mid-size bank with $2B loan book and $500M annual origination.
Document AI pipeline requires training samples of each document type (income proofs, bank statements, title deeds) specific to the bank's market and language
Collections scoring model needs minimum 12 months of delinquency resolution data with contact history and outcome labels
NPA prediction shares behavioral features with the Credit Risk app; deploy together for consistency and avoid duplicate feature engineering
Collateral market value monitoring requires integration with property index providers, gold price feeds, and security market data
Origination funnel analytics require timestamp instrumentation at each stage in the LOS; may need LOS configuration changes
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