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Financial Services · Banking / Securities / Insurance / Consumer Finance

Make financial data truly serve business decisions.

Arisyn Semora combines NL analysis, semantic governance, and multi-table reasoning—turning cross-system, cross-metric, cross-team analysis from “submit & wait” to “ask & get.”

I From complex data to instant decisions—no more long waits for requests, SQL, or reports.

High-risk segment

(last 30 days)

Real-time

analysis

12,486

Auto-aggregates customer, credit, overdue, and transaction data—outputs risk tiers

Business query

NL Query

“Show risk distribution of clients with loans >1M in last 3 months and overdue records.”

Risk tier

changes

Trend

view

Jan

Feb

Mar

Apr

May

Jun

Auto reasoning

flow

6 steps

Intent: client risk analysis ✓

Metrics: loan amount / overdue records                 ✓

Links found: client ↔ loan ↔ repayment                           ✓

Generate & validate SQL   ✓

Core system

Core / Credit / Trade

Client                                                                               tier / AUM

Credit system                                       Loan balance / credit limit

Transaction system                             Fund flows / txn frequency

       — Industry pain points

Real challenges in financial data use

These issues drain resources daily—hurting decision speed and risk control.

🔗

Hard cross-system queries

Full risk checks need core, credit, trading, and anti-fraud data.

Case

When assessing a corporate client’s credit, analysts log into 4 systems, export data, and merge in Excel—takes 2–4h, error-prone.

🔀

Biz–data disconnect

A simple data query typically takes 1–3 days from request to delivery.

Case

PM asks churn (6 mo): request → queue (2d) → SQL (1d) → confirm (0.5d) → revise (0.5d). Data on day 4—window missed.

Lagging risk detection

Signals show up too late—miss the intervention window.

Case

A customer looks normal in core, but overdue in credit. With T+1 sync, risk is found on day 5—overdue already >30 days.

📊

Inconsistent metrics

Same metric defined differently—data conflicts, low trust.

Case

“Corp deposits”: core includes margin; retail excludes; risk removes related-party; KPI adds back. Quarter-end—numbers never match.

       — Use cases

Solve real business problems efficiently

Explore 3 common finance scenarios—see how Arisyn Semora cuts hours to seconds.

01

High-risk scenarios

Smart risk analytics

📋

Business problem

Risk teams need regular portfolio reviews, but rely on data teams for complex SQL across systems—slow, heavy, not real-time for emerging risks.

😤

Traditional approach

Data teams write complex multi-table JOIN SQL (customer, loans, repayments, credit data). One query takes 4–8h (build + debug + run). Any change → rebuild.

Traditional approach

Data teams write complex multi-table JOIN SQL (customer, loans, repayments, credit data). One query takes 4–8h (build + debug + run). Any change → rebuild.

Key capabilities

• Auto cross-table joins (customer + loans + repayments + credit)

• Smart entity & relationship detection

• Support complex conditions & nested queries

🔍

Analytics Platforms

Example query

Input

“Find risk distribution of clients with loans >1M in last 3 months and overdue records; group by client tier and loan type, show default rate.”

Auto-generated SQL

SELECT c.customer_level, l.loan_type, COUNT(DISTINCT c.customer_id) AS total_customers, COUNT(DISTINCT CASE WHEN r.overdue_days > 0 THEN c.customer_id END) AS overdue_customers, ROUND(COUNT(CASE WHEN r.overdue_days > 0 THEN 1 END) * 100.0 / COUNT(1), 2) AS default_rate FROM customer c JOIN loan l ON c.customer_id = l.customer_id JOIN repayment r ON l.loan_id = r.loan_id WHERE l.loan_amount > 1000000 AND l.loan_date >= DATE_SUB(CURRENT_DATE, INTERVAL 3 MONTH) GROUP BY c.customer_level, l.loan_type ORDER BY default_rate DESC;

Results

2,847

Qualified clients

12.3%

Overall default rate

Query time: 0.82s

       — Core value

Quantifiable business impact

Metrics validated from real deployments.

5-10×

Faster queries

From hours to seconds; data team response from 2–3 days to real-time

📉

80%+

Less SQL reliance

Biz users handle 80%+ queries; data teams focus on high-value modeling

🛡️

Real-time

Risk response

From T+1 lag to real-time alerts—earlier risk intervention

60%+

Better data consistency

Unified semantics remove cross-system metric conflicts—reconciliation gaps drop significantly

       — Customer value

Role-based value

Arisyn Semora delivers targeted value for each key role in financial institutions.

Business users

No more barriers to data analysis

No more long waits or complex back-and-forth—just ask and get data.

✓ No SQL needed—just ask

✓ Unlimited iteration—instant results

✓ Auto charts—ready for reporting

✓ 24/7 access—no data team dependency

Data teams

Free from repetitive work

Shift from manual queries to high-value modeling.

✓ Cut 80%+ repetitive SQL
✓ Reusable semantics-auto-sync metric changes
✓ Early quality control—traceable data issues
✓ Focus on modeling & data governance

Management

Faster decisions, lower risk

Data-driven decisions with real-time risk response.

✓ Real-time dashboards—clear at a glance
✓ Live risk alerts—shift from lagging to proactive
✓ Better consistency—more reliable decisions
✓ Faster regulatory reporting—lower compliance risk

Enter the conversational
era of financial data analytics

Try Arisyn Semora now—experience NL-driven insights.
No setup needed—connect in 5 minutes, get results in real time.

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