Bank
Regional Bank
AI Analytics Platform · v4.1
AI GENERATED PRESCRIPTIVE ANALYTICS · TOP REGIONAL BANK
CONFIDENTIAL
FEB 2026
LIVE MODELS
TOTAL DEPOSITS $51.2B ▲+4.1%
LOAN PORTFOLIO $43.8B ▲+6.2%
NIM 3.42% ▲+18bps
SMB APPROVALS 67.3% ▲+9pp
NET CHARGE-OFF RATE 0.38% ▼-12bps
DIGITAL ADOPTION 74.2% ▲+11pp
BRANCH EFFICIENCY 82 pts ▲+14
AI MODEL CONFIDENCE 93.8%
TOTAL DEPOSITS $51.2B ▲+4.1%
LOAN PORTFOLIO $43.8B ▲+6.2%
NIM 3.42% ▲+18bps
SMB APPROVALS 67.3% ▲+9pp
NET CHARGE-OFF RATE 0.38% ▼-12bps
DIGITAL ADOPTION 74.2% ▲+11pp
BRANCH EFFICIENCY 82 pts ▲+14
AI MODEL CONFIDENCE 93.8%
Strategic AI Initiative · 2025–2027

AI Generated
Prescriptive
Analytics for a
Top Regional Bank

Three integrated AI stories driving deposit growth, credit quality improvement, and branch + digital optimization — built on Regional Bank's customer segmentation, SMB credit intelligence, and channel performance data.
STORY 1Deposits & "Own the Middle"
STORY 2SMB Credit & Risk
STORY 3Branch + Digital Optimization
Total Deposits
$51.2B
▲ +4.1% YoY
Loan Portfolio
$43.8B
▲ +6.2% YoY
Net Interest Margin
3.42%
▲ +18bps YoY
Digital Adoption
74%
▲ +11pp YoY
01

Deposit Growth & "Own the Middle" Segmentation

STORY 1 · GROWING THE PORTFOLIO
SITUATION → ACTIONS → IMPACT
Customer Segmentation — "Own the Middle" Framework
DEPOSIT BALANCE DISTRIBUTION BY TIER · REGIONAL BANK RETAIL + SMB
MASS MARKET
$0–25K
⭐ OWN THE MIDDLE
$25K–$500K
HIGH NET WORTH
$500K+
Low share of wallet
High cost to serve
Highest yield
Most acquirable
Fierce competition
from private banks
44%
OF CUSTOMERS
71%
OF DEPOSIT VALUE
2.4x
PRODUCT DEPTH
AI INSIGHT: Middle-tier customers generate 71% of deposit value from 44% of accounts — they are Regional Bank's highest-NIM, most cross-sellable, most retainable segment. Double down here first.
Deposit Growth by Segment — Trend & AI Forecast
QUARTERLY DEPOSIT BALANCE ($B) BY CUSTOMER TIER · 2023–2026 FORECAST
Middle Tier ($25K–$500K)
Mass Market
HNW
AI Forecast
Cross-Sell Penetration by Segment
AVG. PRODUCTS PER HOUSEHOLD · CURRENT VS. AI-TARGETED POTENTIAL
Middle-tier cross-sell gap = 1.4 products/HH · Closing this adds $4.1B in deposit wallet-share
Deposit Retention & Attrition Heatmap
12-MONTH RETENTION RATE BY BALANCE TIER + PRODUCT DEPTH
Customers with 3+ products retain at 94% vs. 61% for single-product — AI cross-sell engine targets 28K mono-product middle-tier HHs
02

SMB Credit Analytics & Risk Intelligence

STORY 2 · RISK + LOSS PERFORMANCE
SITUATION → ACTIONS → IMPACT
SMB Loan Origination — Approval Rate & Loan Volume
QUARTERLY · AI SCORECARD VS. TRADITIONAL UNDERWRITING · 2024–2025
AI Scorecard Volume ($M)
Traditional Volume ($M)
AI Approval Rate (%)
AI scorecard lifted SMB approval rate from 58% → 67% while simultaneously reducing 90-day delinquency — more "yes" with better risk quality.
Credit Risk — Early Warning Signal Performance
NET CHARGE-OFF RATE BY PORTFOLIO COHORT
SMB CHARGE-OFF RATE
0.38%
vs. 0.51% peer avg — AI early-warning flags at-risk accounts 60 days earlier
▼ –12bps YoY improvement
WATCH LIST ACCURACY
88%
of flagged accounts entered 90-day delinquency within 90 days of AI alert
▲ +24pp vs. manual review
TIME-TO-YES (SMB)
3.2d
vs. 14.1 days legacy — AI underwriting auto-approves 61% of clean files
▼ –10.9 days avg turnaround
SMB Risk Scatter — Loan Size vs. Default Probability
EACH BUBBLE = 1 SMB COHORT · SIZE = PORTFOLIO EXPOSURE · COLOR = INDUSTRY SEGMENT
Retail Trade
Healthcare
Hospitality
Construction
High Risk
AI model clusters SMB book into 5 risk bands. Hospitality & high-exposure construction cohorts trigger early-warning alerts 60 days before delinquency.
SMB Credit Funnel — AI-Assisted Underwriting Flow
APPLICATIONS → APPROVALS · MONTHLY AVERAGE · AI VS. LEGACY
Applications Received
All SMB Applications
1,240
100%
AI Pre-Screen Pass
Score ≥ 620
1,017
82%
Auto-Approved
Clean file — instant decision
757
61%
Manual Underwriting
Complex / borderline
260
21%
Final Approvals
Funded — avg 3.2 days
831
67%
Declined
Referred to SBA / alt. products
409
33%
AI underwriting auto-approves 61% of files in <4 hours vs. 14-day legacy avg — freeing underwriters to focus complex cases · Approval rate +9pp
03

Branch + Digital Footprint Optimization

STORY 3 · OPERATIONAL EFFICIENCY
PHYGITAL · CHANNEL INTELLIGENCE

Branch Performance × Customer Density Intelligence Map

NJ / NY / FL FOOTPRINT · AI OPPORTUNITY OVERLAY · HIGH = GREEN · LOW = RED · PROSPECT DENSITY = HEATMAP

HIGH PERFORM
NEEDS INVEST
UNDERPERFORM
HIGH PROSPECT DENSITY
BRANCH PERFORMANCE RANKING
#1
Paramus, NJ
$348M deposits · 2,840 HHs
Score: 94
HIGH PERFORM
#2
Wayne, NJ
$312M deposits · 2,610 HHs
Score: 91
HIGH PERFORM
#3
Midtown Manhattan, NY
$291M deposits · 1,920 HHs
Score: 89
HIGH PERFORM
#4
Hackensack, NJ
$204M deposits · 1,740 HHs
Score: 76
NEEDS INVEST
#5
Clifton, NJ
$188M deposits · 1,580 HHs
Score: 74
NEEDS INVEST
#6
Fort Lauderdale, FL
$162M deposits · 1,340 HHs
Score: 71
NEEDS INVEST
#7
Trenton, NJ
$94M deposits · 1,020 HHs
Score: 48
UNDERPERFORM
#8
Camden, NJ
$72M deposits · 880 HHs
Score: 38
UNDERPERFORM
Union City, NJ ← OPEN
High prospect density · LMI + SMB market
AI: PRIORITY
NEW TARGET
AI recommends: invest in 3 high-density markets, optimize 3 mid-tier branches, and close/merge 2 low-performers — net +$2.8B deposit capacity
Digital Adoption Trend — Phygital Channel Mix
MONTHLY ACTIVE USERS BY PRIMARY CHANNEL · JAN 2024 – JAN 2026
Mobile Primary
Online Banking
Branch Primary
Phygital (Both)
Branch Efficiency Score vs. Prospect Density (Scatter)
EACH DOT = 1 REGIONAL BANK BRANCH · SIZE = DEPOSIT BALANCE · COLOR = PERFORMANCE TIER
High Perform
Needs Investment
Underperform
New Target Market
Quadrant: High Density + Low Efficiency = best investment ROI. AI identifies 4 branches in this quadrant needing $1.2M in targeted upgrades to unlock $3.4B deposit potential.
📱
74%
DIGITAL ADOPTION
4.2 min
DIGITAL ONBOARD TIME
🏦
38%
BRANCH-ONLY CUSTOMERS
🔄
2.8x
PHYGITAL PRODUCT DEPTH
📈
91%
DIGITAL RETENTION RATE
AI

Prescriptive Actions — Situation → Actions → Impact

3 PER STORY · 9 TOTAL
MODEL CONFIDENCE 93.8%
Story 1 · Own the Middle
01
Launch AI-Powered Middle-Tier Acquisition Campaigns Targeting Mass-Affluent Converters
Situation: 44% of Regional Bank HHs sit in the $25K–$500K band but only 38% have 2+ products. Competitors are poaching them with digital-first offers.
Action: Deploy ML propensity model on 280K middle-tier HHs to identify top 20% most likely to add a second product (CD, HELOC, or SMB checking). Trigger personalized offers via mobile app with pre-approved terms in 48 hours.

Impact:
+$4.1Bdeposit wallet-share over 24 months
+1.4 products/HHcross-sell lift in target segment
+18bps NIMfrom CD and HELOC product mix shift
Story 1 · Own the Middle
02
Deploy Real-Time Attrition Alerts for Single-Product Middle-Tier Customers
Situation: 28,000 mono-product middle-tier HHs are at 61% 12-month retention vs. 94% for 3+ product customers — each churned HH costs Regional Bank ~$6,200 in lifetime deposit margin.
Action: Build an early-warning model triggering retention offers when customers show digital disengagement, balance drawdowns >15%, or login frequency drops. Banker alert routes to relationship manager within 24 hours with pre-scripted offer.

Impact:
+33pp retention liftin alerted vs. non-alerted cohort
$174Mannual deposit margin preserved
4,800 HHssaved from churn annually
Story 1 · Own the Middle
03
Price Deposit Products Dynamically for Middle-Tier Segments Using AI Rate Optimization
Situation: Regional Bank's deposit rates are static by product — leaving margin on the table with rate-insensitive HNW customers and over-paying for rate-sensitive mass market accounts.
Action: Implement a segment-aware pricing engine that optimizes CD and MMDA rates by customer lifetime value score, balance tier, and competitive rate environment. Offer best rates to middle-tier customers whose LTV justifies the cost of funds differential.

Impact:
+12bps NIM improvementfrom dynamic pricing optimization
$2.3Bin deposit retention via targeted rate offers
–8bps cost of fundsby reducing over-priced mass market CDs
Story 2 · SMB Credit & Risk
01
Implement AI Scorecard for SMB Underwriting — Reduce Time-to-Yes from 14 Days to 3
Situation: Regional Bank's SMB approval cycle averages 14 days. Competitor fintechs offer same-day decisions. Regional Bank loses 22% of qualified SMB applicants to faster lenders during the wait period.
Action: Deploy gradient-boosting credit scorecard integrating cash flow analytics, industry risk signals, and Dun & Bradstreet data. Auto-approve clean files (<$250K, score ≥680) within 4 hours. Route complex files to specialist with AI-generated decision memo.

Impact:
3.2 days avgtime-to-yes (from 14.1 days)
+9pp approval rate58% → 67% with same risk budget
$840M new SMB loansannual incremental origination
Story 2 · SMB Credit & Risk
02
Deploy Early-Warning System to Cut SMB Charge-Off Rate Below Peer Average
Situation: Regional Bank's SMB NCO rate of 0.38% is below peer average (0.51%) — but the model has not yet been fully deployed across the $8.2B construction and hospitality sub-books where stress is building.
Action: Extend early-warning model to flag 60-day forward delinquency risk across all SMB segments. Auto-trigger proactive outreach — payment restructuring offers, covenant conversations, or referral to SBA workout — 60 days before default. Target 88% precision on watch-list accuracy.

Impact:
–8bps NCO rate0.38% → 0.30% over 18 months
$66Mannual loss avoidance on $8.2B book
60 days earlierintervention vs. manual detection
Story 2 · SMB Credit & Risk
03
Cross-Sell Treasury & Deposits to SMB Borrowers Using Relationship Deepening AI
Situation: 58% of Regional Bank SMB borrowers do not have their operating account at Regional Bank — representing $3.2B in deposit leakage to competitors and leaving cross-sell NIM unrealized.
Action: After loan approval, trigger a 90-day AI-guided onboarding journey to move operating accounts to Regional Bank. Personalize offers (treasury management, payroll, credit cards) based on business type and transaction volume signals. Route high-value SMBs (>$5M revenue) to dedicated relationship manager.

Impact:
$3.2Bdeposit leakage recovery over 36 months
+2.1 products/SMB HHcross-sell depth improvement
+28bps NIMfrom operating deposit funding mix
Story 3 · Branch + Digital
01
Reallocate Branch Investment to High-Density / Low-Efficiency Quadrant Markets
Situation: AI map identifies 4 Regional Bank branches in high-prospect-density markets (Union City, Jersey City LMI corridor, North Miami) that are significantly under-staffed and under-invested relative to their deposit opportunity.
Action: Fund $1.2M targeted investment across 4 branches: bilingual bankers, SMB advisor desks, and digital kiosks. Simultaneously close or merge 2 low-density, low-performance branches (Camden, Trenton North) redirecting $800K in savings to fund the expansion.

Impact:
+$2.8Bdeposit capacity from reallocated footprint
+14 ptsavg branch efficiency score in target markets
$400K net savingsafter reallocation (close vs. open delta)
Story 3 · Branch + Digital
02
Convert 38% Branch-Only Customers to Phygital with AI-Personalized Digital Onboarding
Situation: 38% of Regional Bank customers are branch-primary with no digital engagement — 3x more expensive to serve and 40% less likely to add products vs. phygital customers. These are the highest-churn risk if their branch closes or moves.
Action: At next branch visit or digital touchpoint, trigger a "Digital Starter" offer — one-click mobile setup, first 90 days of digital bill pay free, and a $50 incentive. Use in-branch tablets with banker-assisted digital enrollment. AI segments by age, tech comfort, and product mix to personalize the offer.

Impact:
–$42 cost-to-serveper converted customer per year
2.8x product depthfor phygital vs. branch-only customers
91% retentionfor digitally-engaged customers vs. 68% branch-only
Story 3 · Branch + Digital
03
Launch LMI Digital Mortgage Channel with AI Underwriting to Capture CRA-Credit Growth
Situation: Regional Bank's LMI mortgage origination is below CRA targets in 3 assessment areas. AI analysis identifies $1.1B in unmet LMI mortgage demand in Regional Bank's NJ/NY footprint from qualified borrowers being turned away by manual underwriting bias or process friction.
Action: Launch a digital LMI mortgage channel with alternative credit data (rent pay, utility, gig income) feeding an AI scorecard calibrated for LMI borrower profiles. Partner with HUD-certified counselors for pre-qualification. Set approval target of 72% (up from 54%) with digital decision in 5 days.

Impact:
+$1.1BLMI mortgage origination over 24 months
72% approval ratevs. 54% legacy (alternative data)
CRA Outstandingrating pathway + new $480M deposit base