NexiNexta

Relationship Intelligence for Financial Institutions

A Relationship
Intelligence Layer
Above Core Banking

Explicit, traceable analysis across connected member, account, loan, and household relationships.

Evaluate on Your Data How It Works
How Connected Analysis Works

One default. Four connected exposures.

A borrower misses payments on an auto loan. In a traditional system, that is one delinquent loan. In a relationship graph, it is the start of a traceable exposure path.

Traversal: 3 hops from source record
1
Member A — Auto Loan delinquent
Source record: the loan is linked to Member A in the graph. Status, balance, and payment history are stored on the loan record, traceable to the core export.
2
Spouse B discovered via spouse relationship
Hop 1: traverse the spouse relationship. Spouse B has their own accounts and loans. Household exposure now includes both members.
3
Joint Checking Account via account ownership
Hop 2: both members hold a joint checking account. The account links to both owners in the graph. Balance is shared exposure.
4
Home Equity Loan via collateral relationship
Hop 3: the joint account secures a home equity loan as collateral. Total connected exposure across 3 hops is now visible in one view. Every finding links back to source records.

This traversal uses real relationship types from the NexiNexta graph model: loan ownership, spouse, account ownership, and collateral. Each hop produces a traceable path that can be reviewed and verified against the source export.

Why a Relationship Graph

What it does that warehouse queries do not

Data warehouses are built for aggregation and reporting. NexiNexta is built for multi-hop relationship traversal across connected entities. They complement each other.

Warehouse / SQL
  • —Aggregation, reporting, batch analytics
  • —Relationships require multi-table joins that grow in complexity with each hop
  • —Household and connected-party analysis requires manual stitching per query
NexiNexta Relationship Graph
  • •Multi-hop traversal across members, accounts, loans, and related parties
  • •Relationships are explicit and reusable — defined once, queried from any starting point
  • •Household exposure, co-borrower paths, and connected-entity analysis are native operations

NexiNexta does not replace warehouse reporting. It provides explicit reusable relationship structure for connected analysis that complements what the warehouse already does.

Review Workflows

Two examples of review workflows produced from the demo environment.

Relationship-Aware Credit Decision

Relationship-Aware Credit Decision

Illustrative review workflow: relationship-aware decision support with audit trail.

Traceable Compliance & Data Provenance

Traceable Compliance & Data Provenance

Illustrative review workflow: source-linked findings with recommended actions.

Screens from the NexiNexta demo environment using anonymized data. The dashboard, graph, and analysis assistant shown here are functional components of the current platform build.

Architecture

Where NexiNexta Fits

A relationship intelligence layer that sits above your core systems. It reads exported data. It does not write back to core.

Data Sources (Read-Only Exports)
Core Banking
Members, accounts, loans
Data Warehouse
Reporting, batch analytics
CRM & Lending
Contacts, relationships
NexiNexta
Financial Relationship Graph  ·  Rules Engine  ·  Analysis Assistant
Outputs
Risk & Exposure
Household and connected exposure
Growth Signals
Rules-based opportunity prioritization
Reviewable Reports
Traceable to source records
Analysis Assistant

What is deterministic. What is AI-assisted.

The platform separates deterministic logic from AI-assisted investigation. Both produce reviewable outputs.

Deterministic (Rules-Based)
  • ✔All relationship traversal and graph queries
  • ✔Exposure calculations and risk scoring
  • ✔Trigger rules and opportunity prioritization
  • ✔Data quality validation (14 gates)
  • ✔Source-record linking and audit trail
AI-Assisted (Governed)
  • •Natural-language query translation to graph queries
  • •Queries are validated against the graph schema before execution
  • •Results come from the modeled graph, not generated content
  • •No autonomous decisions, no inference beyond modeled data
  • •All outputs are reviewable and traceable

The Analysis Assistant does not make credit, compliance, or underwriting decisions. It supports natural-language investigation over governed, modeled data. Final judgment remains with the institution.

Security & Governance

Built for regulated financial institutions

Each institution receives a single-tenant deployment on Microsoft Azure with no shared infrastructure across clients.

Isolation & Access
  • ✔Single-tenant Azure deployment per institution
  • ✔No cross-institution data sharing
  • ✔Role-based access control
  • ✔VPN-gated administrative access
Data Protection
  • ✔Encryption at rest and in transit (TLS)
  • ✔Zero public network access on all data stores
  • ✔Private endpoints for database connectivity
  • ✔Managed Identity authentication (no stored access keys)
Audit & Compliance
  • ✔All analysis outputs traceable to source records
  • ✔Audit logging on all platform operations
  • ✔Controlled export and review workflows
Infrastructure
  • ✔Infrastructure defined as code (reproducible per client)
  • ✔Health monitoring with automated alerting
  • ✔Private telemetry ingestion (no public monitoring endpoints)

Full security architecture documentation and infrastructure details are provided during evaluation under NDA.

How It Works

From your data to reviewable results

A defined path from source exports to reviewable relationship analysis. No real-time integration required.

1
Export
Standard data exports from your core banking system. Members, accounts, loans, cards, co-borrowers, joint holders. Uploaded via secure transfer. No API integration required.
2
Map
Source columns are mapped to the canonical relationship model using configurable rules. The mapper supports 15 core banking systems with pre-built column mappings. Unmapped columns are flagged for review.
3
Validate
14 data quality gates check primary key uniqueness, foreign key integrity, enum validation, entity type correctness, and referential completeness before the graph is built.
4
Build
Validated data is constructed into a financial relationship graph with 10 entity types and 14 relationship types. Every vertex and edge is traceable to source records from the export.
5
Analyze
Rules-based analysis evaluates exposure, triggers, and opportunity signals. 10 configurable trigger rules and 10 next-best-action rules run against the graph. Scoring logic is reviewable.
6
Review + Export
Outputs are reviewed in a dashboard with source-record linking. The Analysis Assistant supports natural-language queries over the modeled graph. Results can be exported for downstream workflows.
What Changes for Your Team

Specific outcomes from relationship structure

Find connected exposure that siloed systems miss
Household and co-borrower exposure is computed from explicit graph relationships, not manual SQL joins. A single delinquent loan surfaces all connected positions across the household.
Reduce manual relationship stitching
Spouse, joint-holder, co-borrower, and signer relationships are modeled once and reused across every analysis. Analysts do not rebuild these connections per report.
Surface household opportunities missed in silos
Next-best-action rules evaluate the full household picture — what products both spouses hold, what gaps exist, and whether the household can support the recommendation.
Improve review consistency with traceable outputs
Every finding links back to source records. Review workflows show the relationship path, the rule that fired, and the data that triggered it. Consistent across analysts and reviewers.
What NexiNexta Is Designed To Do
  • ✔Structure connected financial relationships for governed analysis
  • ✔Support review workflows with traceable, source-linked outputs
  • ✔Surface exposure and opportunity signals across connected records
  • ✔Complement warehouse reporting with reusable relationship structure
What NexiNexta Does Not Replace
  • —Core banking systems
  • —Institution underwriting authority
  • —Compliance judgment or final decisions
  • —Existing data warehouse reporting
Get Started

Structured Pilot on Your Data

The pilot uses your exported data to build a relationship model, validate outputs against source records, and produce reviewable results your team can inspect.

Pilot Inputs

Standard exports from your core banking system mapped to the relationship model.

Typical data:
  • ▸Members, accounts, loans
  • ▸Co-borrowers, spouses, joint holders
  • ▸Cards, products, insurance
No API integration required for the structured pilot.
What You Get Back
  • ✔Household and connected exposure analysis verifiable against source records
  • ✔Relationship opportunity list with reviewable rules-based prioritization
  • ✔Dashboard access for review during evaluation
  • ✔Data quality report from 14-gate validation pipeline
Scoped
Defined timeline
Configurable
Schema-adaptive mapping
Single-Tenant
Isolated Azure environment
1 Sponsor
CRO, CLO, or CIO

Evaluate NexiNexta on your data

See household exposure, connected exposure paths, and relationship-based opportunity signals built from your institution's exported data.

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Designed for evaluation by CIO, CRO, CLO, and analytics leaders.