The strategic value of FinTech-CRM integration is clear in theory. But theory doesn't pay the bills, reduce risk, or win competitive battles. Results do.
The strategic value of FinTech-CRM integration is clear in theory. But theory doesn't pay the bills, reduce risk, or win competitive battles. Results do.
This case study collection examines four financial services firms that successfully integrated FinTech innovations with Salesforce Financial Services Cloud, delivering measurable business outcomes. Each case provides detailed context on the challenge, implementation approach, technical architecture, and quantified results—offering practical blueprints for firms embarking on similar journeys.
These aren't sanitized vendor success stories. They're realistic accounts that include challenges encountered, decisions made, and lessons learned. The outcomes are impressive, but more importantly, they're achievable for firms willing to invest strategically and execute disciplinarily.
Firm: Summit Wealth Partners (pseudonym)
Type: Independent RIA
AUM: $3.2 billion
Advisors: 45
Locations: 8 offices across Southeast U.S.
Summit Wealth Partners faced a critical growth constraint: client onboarding took an average of 18 days from initial consultation to funded account. This extended timeline created three significant problems.
First, they faced a competitive disadvantage as prospective clients increasingly compared their experience to digital-first competitors completing onboarding in just two to three days. Second, the process created an advisor productivity drain, with each new account requiring four to six hours of advisor time for form completion, document collection, and manual data entry. Third, manual processes led to compliance risk, with 23% of new accounts requiring remediation during compliance audits.
The firm's existing Salesforce implementation tracked client relationships but wasn't connected to account opening or compliance workflows. Advisors described feeling like they were "managing three separate systems that don't talk to each other."
Summit partnered with Vantage Point to design and implement a comprehensive FinTech-CRM integration connecting Salesforce Financial Services Cloud as the central relationship management platform, AlloyCard for identity verification and KYC/AML compliance, DocuSign for electronic signature and document management, Advisor360° for account opening and portfolio onboarding, and MuleSoft as the integration layer enabling seamless data flow.
The process flows seamlessly: advisors initiate onboarding in Salesforce, data flows to AlloyCard for identity verification, verified data populates DocuSign account opening forms, clients receive mobile-optimized document packages, completed forms trigger Advisor360° account creation, account details sync back to Salesforce, and advisors receive notifications to schedule welcome calls.
Key integration patterns included real-time identity verification with results returned within five seconds, dynamic document generation that automatically selects and pre-populates appropriate forms, status tracking and notifications through Platform Events, and automated compliance validation ensuring all required documents are collected before account creation.
The 16-week implementation moved through four distinct phases. Discovery and design spanned weeks one through four, mapping the current onboarding process, identifying 47 distinct form variations, designing the future state that eliminated 72% of manual touchpoints, and creating detailed data mappings.
Build and configuration occupied weeks five through ten, implementing the MuleSoft integration layer, configuring AlloyCard decisioning rules, creating DocuSign templates for 12 account types, building custom Salesforce Lightning components, and developing automated compliance validation rules.
Testing and pilot consumed weeks 11 through 14 with comprehensive testing across over 150 scenario variations, pilot deployment with three offices and 12 advisors, workflow refinement based on feedback, and resolution of 23 issues discovered during the pilot.
Production deployment in weeks 15 and 16 featured phased rollout to remaining offices, training delivery to all advisors and operations staff, implementation of monitoring dashboards, and establishment of support protocols.
The quantitative outcomes measured 12 months post-implementation were striking. Average onboarding time dropped from 18 days to 3.5 days, an 81% reduction. Advisor hours per account fell from 4.6 hours to just 0.8 hours, an 83% reduction. New accounts opened annually increased from 287 to 421, a 47% increase. Onboarding abandonment rate decreased from 31% to 12%, a 61% reduction. Compliance remediation requirements dropped from 23% to 4%, an 83% reduction. The first-year revenue impact from new capacity reached $1.8 million.
Qualitative outcomes were equally impressive. Advisor Net Promoter Score for internal platform usability increased from 23 to 67, with advisors reporting they spent recovered time on client relationship development and prospecting. Client satisfaction scores for the onboarding process jumped from 6.2 out of 10 to 8.9 out of 10, with positive reviews frequently mentioning an "easy, professional, modern experience."
The firm's compliance posture improved dramatically, passing an SEC examination with zero findings related to onboarding and documentation—the first clean exam in the firm's history. From a competitive positioning standpoint, they won 14 client relationships specifically citing superior onboarding experience versus competitors, representing an estimated $85 million in new AUM.
Total Year 1 investment reached $325,000, including MuleSoft licensing at $55,000, AlloyCard integration and annual fees of $42,000, DocuSign enterprise licensing at $28,000, Advisor360° integration at $35,000, and Vantage Point implementation services at $165,000. Annual ongoing costs totaled $135,000 for software licenses and maintenance.
First-year financial benefits totaled $2,125,000, including $1,800,000 in revenue from 134 additional accounts, $250,000 in advisor capacity value from 278 freed hours, and $75,000 in compliance remediation cost avoidance.
This delivered a net ROI of 554% in Year 1 with a payback period of just 2.8 months.
Success factors included executive sponsorship with the Managing Partner personally championing the initiative, advisor involvement through an advisory council providing input throughout design, a phased approach with pilot testing before full rollout, training investment combining in-person sessions with recorded videos and quick-reference guides, and continuous improvement through monthly reviews identifying and implementing 18 workflow enhancements in the first six months.
What worked well included starting with one complex use case rather than trying to integrate everything at once, investing in a proper integration layer from the beginning to avoid point-to-point complexity, and building custom Salesforce UI components that surfaced integration status without requiring advisors to understand underlying complexity.
What they'd do differently: involve the compliance team earlier in the design phase to avoid late-stage requirements additions, create more comprehensive training materials before go-live rather than building reactively, and engage with the custodian earlier to streamline the final account activation step which remains partially manual.
Firm: First Community Bank (pseudonym)
Type: Community bank with commercial lending focus
Assets: $4.2 billion
Loan Officers: 32
Branches: 18
First Community Bank built its reputation on relationship-driven commercial lending. However, as loan volumes grew and competition intensified, their manual processes couldn't scale. Key pain points included slow credit decisioning taking five to seven days per application, inconsistent risk assessment with six different credit analysts applying varying standards, limited data utilization with credit decisions based primarily on financial statements while missing alternative data signals, and disconnected systems with nCino managing loan workflow, Salesforce tracking relationships, and credit analysis in Excel spreadsheets.
The bank was losing deals to faster competitors while simultaneously taking on potentially higher risk due to inconsistent analysis. The Chief Credit Officer described it as "trying to be both conservative and competitive with stone-age tools."
First Community Bank integrated an AI-powered credit analysis FinTech (Ocrolus with custom ML models) with their existing Salesforce FSC and nCino infrastructure. The integrated technology stack included Salesforce Financial Services Cloud for relationship management, nCino Bank Operating System for loan origination workflow, Ocrolus for document processing and data extraction, custom ML models built on AWS SageMaker for risk scoring and decisioning, Plaid for alternative data through bank transaction analysis, and MuleSoft for orchestration and data flow.
The architecture flows seamlessly from loan officer initiating an application in Salesforce through opportunity syncing to nCino, borrower document uploads, Ocrolus financial data extraction, Plaid bank account connections with consent, alternative data feeding into the custom ML risk model, risk score and analysis returning to Salesforce and nCino, with loan officers receiving preliminary decisions in under two hours and credit team reviewing only flagged applications.
The key innovation was a risk scoring algorithm analyzing 127 variables across multiple dimensions: traditional financial ratios, cash flow patterns from bank transactions, payment history trends, industry benchmarking, macroeconomic indicators, and relationship history with the bank. The model was trained on eight years of historical loan performance data covering over 4,200 loans, achieving 89% accuracy in predicting probability of default—significantly outperforming traditional credit scoring.
The 22-week implementation began with data science and model development in weeks one through six, aggregating historical loan data, cleaning and normalizing it, developing the baseline ML model using Python and TensorFlow, validating model accuracy, refining based on credit team feedback, and deploying to AWS SageMaker production.
Integration development occupied weeks seven through 14, building MuleSoft flows connecting all systems, creating custom Salesforce Lightning components for risk score visualization, implementing model monitoring infrastructure, developing explainability dashboards, and building override capability for credit team manual decisions.
Testing and validation in weeks 15 through 18 included parallel processing of 50 active applications through old and new processes, Model Validation Officer review for fairness and compliance, refinement of decision thresholds based on risk appetite, and user acceptance testing with loan officers.
Deployment and rollout covered weeks 19 through 22 with production deployment and monitoring, phased rollout starting with lower-risk renewal loans, expansion to new originations after 30-day validation, and full production deployment to all loan officers.
Quantitative outcomes measured 18 months post-implementation showed remarkable improvements. Average credit decision time dropped from 5.7 days to 4.2 hours, 93% faster. Loan officer productivity increased from 2.8 loans per month to 5.3 loans per month, an 89% increase. Annual loan originations grew from $287 million to $441 million, a 54% increase. Credit loss rate fell from 1.8% to 0.9%, a 50% reduction. Operating expense ratio improved from 2.4% to 1.9%, a 21% improvement. Time to loan approval decreased from 18 days average to 7 days average, 61% reduction.
Qualitative outcomes included competitive wins with the bank estimating 27 deals won specifically because of faster credit decisions, totaling $89 million in new loans. Credit quality improved as machine learning identified subtle warning signs human analysts missed, all without reducing approval rates. Regulatory compliance strengthened with model explainability features enabling clear documentation, resulting in an OCC examination passing with commendation for innovative approach.
Loan officer satisfaction showed 87% feeling more confident in credit recommendations and appreciating faster turnaround for clients. Fair lending analysis showed no adverse impact to protected classes, with approval rates for minority-owned businesses actually increasing 12% while maintaining the same risk profile.
Total Year 1 investment reached $670,000, including ML model development at $185,000, AWS SageMaker annual cost of $42,000, Ocrolus licensing at $55,000, Plaid data access at $38,000, MuleSoft implementation at $125,000, and Vantage Point services at $225,000. Annual ongoing costs totaled $210,000 for software licenses, cloud services, and model monitoring.
First-year financial benefits reached $10,740,000, including $8,470,000 in interest income from an additional $154 million in loans at 5.5% average rate, $1,850,000 in credit loss reduction, and $420,000 in operating efficiency gains.
This delivered a net ROI of 1,503% in Year 1 with a payback period of just 3.1 weeks.
Success factors included establishing a Model Risk Management framework before deployment ensuring regulatory compliance, maintaining human oversight with experienced credit team involvement for exceptions and override capability, investing heavily in model explainability for credit officers and regulators, positioning technology as empowering loan officers rather than replacing them, and conducting monthly model performance reviews with quarterly retraining based on new data.
What worked well included starting with loan renewals for known customers before new originations to reduce risk during validation, building a custom ML model rather than using off-the-shelf solutions to optimize for the bank's specific portfolio, and creating extensive documentation that paid dividends during regulatory examination.
What they'd do differently: involve legal and compliance earlier in data acquisition strategy as they had to remove certain alternative data sources due to FCRA concerns, allocate more budget for change management and training as loan officers needed more support adapting to AI-assisted processes, and integrate earlier with the loan servicing system to enable closed-loop feedback on model accuracy.
Firm: Heritage Insurance Group (pseudonym)
Type: Independent insurance agency
Premium Volume: $125M annually
Agents: 68
Lines of Business: Personal lines, commercial lines, benefits
Heritage Insurance Group struggled with fragmented client communication. Core problems included channel proliferation with clients contacting the agency via phone, email, text, web portal, and social media but agents unable to track conversation history across channels. Response time variability meant some inquiries were answered in hours while others took days depending on which channel was used and which agent saw it first.
Lost opportunities occurred as agents missed cross-sell and renewal opportunities because they lacked visibility into the client's full relationship across all lines of business. Compliance gaps emerged as scattered communication made it difficult to demonstrate proper notice and documentation for regulatory audits.
Service quality was inconsistent, client satisfaction declining, and retention rates suffering. The CEO noted: "We're great at insurance, but we're mediocre at client experience—and in 2025, that's not good enough."
Heritage integrated multiple communication FinTech solutions with Salesforce to create a unified client interaction platform. Integrated components included Salesforce Financial Services Cloud as the central platform, RingCentral for phone system with screen-pop integration, SMS-Magic for two-way SMS communication, Sprinklr for social media monitoring and response, Salesforce Service Cloud for case management, Applied Epic agency management system for policy details, and MuleSoft for bidirectional data sync.
The unified experience architecture ensures that when clients contact Heritage via any channel, communication is captured in Salesforce with full context, agents see complete history across all channels, agents have access to all policies, quotes, and claims in a single view, responses are tracked with SLA monitoring, and all communication is stored for compliance and training.
Key capabilities delivered included a 360-degree client view showing policies, quotes, claims, and communication history across all channels in a single dashboard, intelligent routing of inbound communication to agents with the strongest relationship and appropriate expertise, SLA management with automated tracking and escalation for aging inquiries, omnichannel presence allowing agents to respond via any channel from a single interface, and AI-powered insights with Einstein analyzing communication patterns to surface renewal risks and cross-sell opportunities.
The 14-week implementation began with a communication audit in weeks one through three, analyzing six months of client communication across all channels, identifying 12 distinct communication paths creating silos, mapping agent workflow and pain points, and designing the unified communication experience.
Platform integration occupied weeks four through nine, integrating RingCentral with Salesforce for click-to-dial and call logging, implementing SMS-Magic for text messaging capability, connecting Sprinklr for social media monitoring, configuring Service Cloud cases with automated routing rules, and building Applied Epic integration for real-time policy data access.
Agent training in weeks 10 through 12 included creating role-based training modules, delivering hands-on training to all agents in small groups, developing quick-reference guides and video tutorials, and designating super users for ongoing peer support.
Production launch covered weeks 13 and 14 with deployment to all agents simultaneously in a big bang approach due to the integrated nature, intensive support for the first two weeks, and daily stand-ups to address issues and refine workflows.
Quantitative outcomes measured 12 months post-implementation showed dramatic improvements. Average response time dropped from 23 hours to 2.4 hours, 90% faster. First contact resolution increased from 42% to 71%, a 69% improvement. Client satisfaction measured by NPS increased from 28 to 54, a 93% improvement. Retention rate grew from 87.2% to 92.8%, a 6.4% increase. Cross-sell rate jumped from 18% to 34%, an 89% increase. Agent productivity increased from 127 clients per agent to 189 clients per agent, a 49% increase.
Qualitative outcomes showed clients consistently praising the "seamless" experience regardless of contact channel, with positive online reviews increasing 340%. Agent satisfaction scores increased from 6.1 out of 10 to 8.7 out of 10, with comments frequently mentioning "finally having everything in one place."
The agency successfully passed a state DOI market conduct examination with zero citations—the first time in five years. From a competitive differentiation standpoint, the agency began marketing "5-Star Client Experience" based on omnichannel capabilities, winning accounts from competitors.
Total Year 1 investment reached $440,000, including RingCentral integration at $42,000, SMS-Magic licensing at $18,000, Sprinklr social media platform at $55,000, Service Cloud licenses at $95,000, MuleSoft implementation at $85,000, and Vantage Point services at $145,000. Annual ongoing costs totaled $215,000 for software licenses, maintenance, and support.
First-year financial benefits reached $10,370,000, including $7,000,000 in retained premium from 5.6% retention improvement, $2,850,000 in new premium from increased cross-sell, and $520,000 in productivity gain value from reduced staffing needs.
This delivered a net ROI of 2,257% in Year 1 with a payback period of just 2.1 weeks.
Success factors included conducting a comprehensive communication audit to understand all existing paths before designing the solution, having agents participate in co-designing the unified interface to ensure it met real-world needs, using big bang deployment across all agents simultaneously to prevent bridging between old and new systems, and providing intensive launch support with the implementation team on-site for the first two weeks to accelerate adoption.
What worked well included simplifying the agent interface to a single screen which reduced training burden and accelerated adoption, implementing automated routing based on relationship history which improved client experience without requiring agent coordination, and building compliance reporting into the platform from day one to avoid retrofitting later.
What they'd do differently: start with phone and email integration first, then add SMS and social media later as trying to do everything at once was ambitious, allocate more time for testing edge cases in routing rules since several early cases went to the wrong agent, and implement more sophisticated AI capabilities for predictive insights as the current implementation is relatively basic.
Firm: Pinnacle Family Office (pseudonym)
Type: Multi-family office serving UHNW families
AUM: $8.7 billion
Client Families: 47
Professionals: 85 (including advisors, tax, legal, concierge)
Pinnacle Family Office provided white-glove service to ultra-high-net-worth families, coordinating complex needs across investment management, tax planning, estate planning, philanthropy, and lifestyle services. Each client family averaged 12.5 professional touchpoints monthly.
Strategic challenges included a scalability constraint where growth was limited by professional capacity—they couldn't add families without proportionally adding staff. Service inconsistency meant quality varied based on individual professional capabilities with no systematic approach to ensuring comprehensive service. Data fragmentation scattered investment data in Black Diamond, tax information in CCH ProSystem, estate documents in NetDocuments, and lifestyle requests in email with no unified view. Inefficient workflows saw routine tasks like performance reporting, meeting scheduling, and document retrieval consuming 40% of professional time.
The managing partner articulated the dilemma: "We're profitable, but we can't scale this model. We either need to raise minimum relationship size to $100M or find a way to serve clients more efficiently without sacrificing quality."
Pinnacle integrated multiple specialized FinTech solutions with Salesforce to create an intelligent service delivery platform. The integrated ecosystem included Salesforce Financial Services Cloud as the orchestration hub, Black Diamond for portfolio management and reporting, CCH ProSystem for tax planning, NetDocuments for document management, Calendly for automated scheduling, Einstein AI for service recommendations and predictions, Agentforce for routine client inquiries, Slack for internal team collaboration, and MuleSoft for integration.
The intelligent service model works by identifying client needs either proactively or reactively, having AI analyze the need and recommend appropriate services, notifying relevant professionals with context and suggested actions, automatically creating and routing tasks, aggregating data from multiple systems into a unified view, proactively communicating with clients, tracking and measuring service delivery, and capturing insights for continuous improvement.
Key automation capabilities included proactive service triggers with Einstein AI monitoring client data for 47 "service moments" like market volatility, approaching tax deadlines, or outdated estate plans, then proactively alerting professionals. Intelligent document assembly automatically pulled data from Black Diamond and formatted it appropriately when clients requested financial statements, all without professional involvement.
Meeting preparation automation aggregated performance data prior to quarterly review meetings, identified discussion topics based on client goals, and pre-populated meeting agendas. Routine inquiry handling through Agentforce agents addressed common client questions about account balances, recent transactions, and document retrieval through conversational interface, escalating complex queries to humans.
The 28-week implementation timeline was longer due to complexity and security requirements. Service design in weeks one through six cataloged all services provided across 47 client families, identified 127 distinct service activities, grouped them into eight service categories with defined workflows, designed AI-powered recommendation engine logic, and established service quality metrics and SLAs.
Platform development covered weeks seven through 16, implementing MuleSoft integration to all source systems, developing custom Salesforce objects for service tracking, training Einstein AI models on historical service data, configuring Agentforce agents for common inquiry types, building the professional dashboard for service orchestration, and implementing Slack integration for team collaboration.
Testing and pilot in weeks 17 through 22 included extensive testing across multiple service scenarios, pilot deployment with five client families representing diverse needs, refinement of AI models based on professional feedback, and adjustment of workflows to match real-world complexity.
Production rollout spanned weeks 23 through 28 with phased deployment by service category, comprehensive professional training, client communication about the enhanced service model, and intensive monitoring and optimization.
Quantitative outcomes measured 18 months post-implementation showed impressive scaling. Client families served increased from 47 to 72, a 53% increase. Professional efficiency improved from 60% billable time to 78% billable time, a 30% improvement. Average service touches per family per month increased from 12.5 to 18.7, a 50% increase. Client satisfaction grew from 8.2 out of 10 to 9.4 out of 10, a 15% improvement. Routine task automation jumped from 12% to 67%, a 458% improvement. Service request response time dropped from 18 hours average to 2.1 hours average, 88% faster.
Qualitative outcomes demonstrated scalability achieved by adding 25 new client families without proportional staff increase, with professional-to-client ratio improving from 1.8:1 to 1.2:1. Service quality improved as proactive service triggers ensured comprehensive coverage—no client tax deadlines were missed, every significant market event was addressed, and all estate plans were reviewed on schedule.
Professional satisfaction reached 91% feeling technology enhanced their ability to serve clients, with comments praising "getting back to advice and planning instead of administrative work." Client retention achieved zero departures in 18 months post-implementation versus two to three annually pre-integration, with existing families referring eight new relationships.
The competitive position strengthened by lowering minimum relationship size from $50 million to $30 million while maintaining margins, opening access to a new client segment.
Total Year 1 investment reached $1,265,000, including MuleSoft enterprise platform at $125,000, Einstein AI and Agentforce licensing at $185,000, custom development and configuration at $420,000, NetDocuments integration at $55,000, Black Diamond API development at $95,000, and Vantage Point services at $385,000. Annual ongoing costs totaled $500,000 for software licenses, platform maintenance, and AI model monitoring.
First-year financial benefits reached $8,300,000, including $6,250,000 in revenue from 25 additional families at an average $250,000 fee per family, $850,000 in efficiency gain value from deferred hiring, and $1,200,000 from service quality improvement impacting retention.
This delivered a net ROI of 556% in Year 1 with a payback period of 7.6 weeks.
Success factors included service-first design starting with ideal client experience and working backward to technology rather than vice versa, professional involvement with co-designed workflows ensuring technology enhanced rather than constrained their judgment, incremental rollout by service category allowing refinement before full scale, AI transparency allowing professionals to see why AI made recommendations building trust in the system, and client communication positioning enhancements as "investing in serving you better" to secure client buy-in.
What worked well included focusing on automating routine tasks first to free professionals for higher-value services, building AI recommendations as suggestions rather than mandates to maintain professional autonomy while providing decision support, and extensive testing with pilot families that identified edge cases before full deployment.
What they'd do differently: implement document management integration earlier as they waited until Phase 2 but it should have been Phase 1 priority, invest more in change management for senior professionals who were most resistant to AI-powered tools, and integrate earlier with wealth transfer FinTech for estate planning workflows which currently remains a manual bottleneck.
While each case study addresses different challenges in different contexts, several consistent success factors emerge across all four implementations.
None of these firms started with "we need to integrate these specific systems." They started with business problems: Summit wanted faster onboarding to win more clients, First Community needed scalable credit decisioning, Heritage required consistent client communication, and Pinnacle sought service efficiency to enable growth. Technology was the means to achieve business ends, not an end itself.
All four cases used MuleSoft as integration middleware. While upfront investment was significant ranging from $85,000 to $125,000, it paid dividends through faster implementation of subsequent integrations, centralized monitoring and error handling, reduced technical debt versus point-to-point integrations, and ability to swap underlying systems without disruption. Firms trying to avoid integration layer costs typically spend more in the long run maintaining brittle connections.
Successful AI implementations positioned technology as a decision support tool, not an autonomous decision maker. First Community's credit model recommended decisions but the credit team could override. Pinnacle's service recommendations were suggestions that professionals acted on. Heritage's routing was intelligent but agents could manually reassign. This preserved professional autonomy while providing powerful assistance.
The technical work—APIs, integrations, data flows—was only half the battle. Equally critical were executive sponsorship securing organizational commitment, user involvement in design ensuring practical workflows, comprehensive training building capability and confidence, and ongoing support addressing issues and optimizing adoption. Firms that under-invested in change management struggled with adoption despite technically sound implementations.
Each firm defined clear success metrics before implementation across operational efficiency measuring time, cost, and capacity, financial performance tracking revenue, margin, and retention, quality indicators monitoring satisfaction, errors, and compliance, and strategic outcomes assessing growth and competitive position. Post-implementation measurement validated investment and identified optimization opportunities.
None of these implementations were "done" at go-live. Each established processes for regular performance review and optimization, user feedback collection and response, quarterly enhancement planning, and annual strategic reassessment. Technology platforms are living systems requiring ongoing cultivation.
These four case studies demonstrate that strategic FinTech-CRM integration delivers measurable, substantial business value across diverse financial services contexts. Wealth management reduced onboarding time by 81%. Commercial banking improved loan productivity by 89%. Insurance increased retention by 6.4% through better communication. Family office scaled client service by 53% without proportional cost increase.
The patterns are clear. The approaches are proven. The results are achievable.
The question isn't whether integration delivers value—these cases prove it does.
The question is: Will your firm be an early mover capturing competitive advantage, or a laggard forced to catch up?
The firms profiled in these case studies weren't necessarily the largest, best-capitalized, or most technically sophisticated in their markets. But they shared common characteristics: clarity of purpose on what they wanted to achieve, willingness to invest in both technology and change management, commitment to execution with appropriate resources and timeline, and openness to partnership with experts who'd navigated these journeys before.
If your firm shares these characteristics, you have everything needed to create your own success story.
Connect with Vantage Point to explore how strategic FinTech-CRM integration can drive measurable outcomes for your firm.
Email: sales@vantagepoint.io
Phone: +1 469-499-3400
Web: vantagepoint.io
Copyright 2025 © Vantage Point. All rights reserved.
David Cockrum is the founder of Vantage Point and a former COO in the financial services industry. His operational and compliance background informs Vantage Point's best practice frameworks, ensuring implementations balance technical excellence with regulatory adherence and risk mitigation.
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