How to Build a Robo-Advisor Platform: Everything You Need To Know

Are you struggling to keep up with clients who expect personalized investment advice at the speed of a swipe?

Or are you questioning whether traditional advisory models can compete with the digital-first expectations of millennials and Gen Z?

The financial industry is shifting fast, and robo advisor platforms are changing how financial institutions deliver advice, scale services, and reduce operational costs.

According to Fortune Business Insights, the global robo advisory market, valued at USD 8.39 billion in 2024, is expected to reach USD 69.32 billion by 2032, growing at a CAGR of over 30 percent. That surge is proof of how quickly investors are embracing digital wealth tools.

For decision-makers at banks, fintech firms, and wealth management companies, Robo advisors provide intelligent, automated, and highly secure investment solutions that appeal to today’s digital-savvy investors. When you layer in Robo advisor software development using AI, you unlock personalization and predictive insights that human advisors alone cannot deliver.

Whether your goal is to attract younger clients, expand offerings, or cut costs without cutting quality, the timing couldn’t be better. With the right partner, like an AI development company that understands compliance, scale, and security, you can move from idea to impact faster.

Forward-thinking firms are already tapping into AI automation services to accelerate adoption. Banks and fintechs that adopt AI Powered Wealth Management Software Solutions are gaining a competitive edge by meeting digital-native expectations head-on.

This blog will walk you through steps to develop a robo advisor platform for financial businesses, key features to consider, the technology behind it, and the cost implications. Think of it as a single resource that answers everything you and your team might want to know before making the leap.

What Is a Robo Advisor Platform?

A robo advisor platform is a digital system that provides automated investment advice and portfolio management with little to no human intervention. Instead of relying on a financial advisor to build strategies manually, clients interact with an interface that gathers their financial goals, risk tolerance, and preferences.

Behind the scenes, algorithms and artificial intelligence process that data to recommend diversified portfolios, rebalance accounts, and optimize returns.

Unlike generic financial apps, robo advisor platforms are built specifically for investment services. They combine the discipline of modern portfolio theory with real-time market data and AI-driven insights. This makes them reliable, consistent, and accessible to both small investors and large institutions.

The appeal lies in scale. These AI platforms can:

• Serve thousands of clients at the same time
• Maintain compliance and data security across every account
• Deliver personalized investment recommendations
• Lower operational costs without lowering service quality

For clients, this means 24/7 access to tailored strategies at a fraction of the cost of traditional advisors.

Firms often partner with a custom software development company to design robust architectures that handle transactions securely while integrating with market feeds, custodians, and compliance systems. To take personalization further, many teams also leverage AI integration services that enable advanced risk modeling and predictive analytics within the platform.

At its core, a robo advisor platform is a fully automated investment ecosystem designed to scale, comply, and evolve with client needs.

How Does a Robo Advisor Platform Work?

At its core, a robo advisor platform replaces manual investment planning with a seamless, automated process. The flow usually starts when a client signs up and answers a series of questions about financial goals, investment horizon, and risk tolerance. The AI platform collects this data through a user-friendly digital interface.

Once the inputs are in place, algorithms take over. They evaluate the client’s profile, match it against market conditions, and generate a portfolio designed to balance growth and risk. The portfolios are often built using ETFs, index funds, or other low-cost instruments.

Here’s how the process unfolds step by step:

• Data Collection:Clients provide their goals, income levels, time horizon, and comfort with risk.
• Algorithmic Portfolio Design:AI-driven models analyze the data to create tailored investment strategies.
• Automation at Scale:Rebalancing, tax-loss harvesting, and performance tracking run in the background without human intervention.
• Continuous Optimization:AI learns from client activity and market signals to refine recommendations.

Many firms use AI automation services to handle repetitive yet critical functions like compliance checks and transaction monitoring. The result is a platform that feels simple on the surface yet is incredibly sophisticated under the hood. Clients see intuitive dashboards, while businesses benefit from scalable systems capable of serving thousands of investors at once.

Why Invest in Robo Advisor Software Development?

Many firms are already integrating enterprise AI solutions to strengthen compliance and analytics, while intelligent AI agents are making these platforms smarter and more interactive.

Before diving into features and development, it is important to understand why different types of financial institutions are adopting robo advisors at such speed. Broadly, all financial businesses benefit from scalability and cost reduction, while banks and credit unions are under unique pressure to stay relevant in a world of fintech disruptors. Let’s break down both perspectives.

Why Financial Businesses Can’t Afford to Ignore Robo Advisors?

For fintech startups, wealth managers, and investment firms, robo advisors are no longer optional. The demand for fast, personalized, and affordable investment services is rising, and manual processes can’t keep up.

Here’s why businesses are embracing these platforms:

• Serve more clients without scaling staff:A single system can onboard and manage thousands of investors simultaneously, ensuring growth isn’t tied to headcount.
• Lower costs through automation:Tasks like portfolio rebalancing, compliance checks, and reporting are handled automatically, reducing reliance on expensive advisory teams.
• Personalize at scale with AI insights:Algorithms adjust portfolios in real time based on client behavior and market trends, delivering the kind of customization clients now expect.
• Maintain compliance while growing:Automated workflows keep records accurate and consistent, helping firms meet strict financial regulations even when handling high volumes.

With advanced AI model development, financial businesses can go beyond static investing and introduce predictive features that anticipate market shifts.

Why Banks and Credit Unions Should Build Robo Advisor Platforms?

Banks and credit unions face unique challenges. Traditional strengths such as branch networks and face-to-face relationships no longer appeal to younger investors who expect instant, app-based financial guidance.

Robo advisors help close this gap by enabling banks to:

• Deliver digital-first experiences:Clients can receive investment guidance anytime, without waiting for an in-person consultation.
• Attract and retain younger investors:Millennials and Gen Z are far more likely to trust platforms that feel intuitive, affordable, and available 24/7.
• Compete with fintech disruptors:By offering their own robo advisor platforms, banks prevent clients from shifting assets to modern fintech rivals.

Enhancements like conversational onboarding, built by an AI chatbot development company, can further improve engagement. Some institutions even hire AI developers to design bespoke robo advisors that integrate seamlessly with core banking systems.

For banks and credit unions, robo advisors are more than a way to cut costs. They are a strategic tool to remain trusted financial partners in an increasingly digital-first market. And to understand their growing appeal even better, it helps to compare how robo advisor platforms stack up against traditional financial advisors.

Robo Advisor Platform Development vs Traditional Financial Advisors

Traditional advisors have long been the backbone of investment guidance. They bring human judgment, personal relationships, and trust to the table. Yet, their approach has limitations in today’s fast-paced, digital-first environment. Robo advisor platforms, on the other hand, offer automation, scalability, and accessibility that human advisors alone cannot match.

Here’s how they are different from each other:

Businesses are learning how to build AI software that can automate decision-making without compromising accuracy. Many are also finding new ways to integrate AI into an app, turning a standard advisory platform into a responsive, intelligent system that evolves with investor needs.

Human advisors remain valuable for complex planning, while robo advisor platforms bring the speed and scale modern investors expect. To see how businesses can leverage this advantage, it helps to look at the different types of robo advisor platforms available today.

Types of Robo Advisor Platforms With Real-World Examples

Not all robo advisors are built the same. Depending on the target audience, business model, and level of automation, different types of platforms emerge. Here are the three most common models dominating the market today:

1. Hybrid Robo Advisors

Hybrid robo advisors blend automation with human oversight. Clients receive algorithm-driven portfolio recommendations, but also have access to a human advisor for reassurance or complex queries.

Example: Vanguard Personal Advisor Services combines digital advice with certified professionals.

Who it benefits: Traditional wealth managers and banks that want to maintain a human touch while scaling digital services.

This model works well for clients who want the efficiency of technology but still trust a person for high-stakes decisions.

2. Pure Digital Robo Advisors

Pure digital robo advisors operate entirely through algorithms, with little or no human intervention. These platforms are designed for tech-savvy investors who prefer full self-service experiences.

Example: Betterment and Wealthfront lead this category, offering end-to-end investment management through apps.

Who it benefits: Fintech startups and firms targeting millennials and Gen Z investors who prioritize cost and convenience.

Adding AI-driven engagement tools, such as an AI conversation app, can make these platforms more interactive, helping businesses create deeper client relationships without human staff.

3. White-Label & Custom Robo Advisors

Some firms prefer to launch their own branded robo advisors rather than relying on existing platforms. White-label and custom-built solutions allow businesses to design features around compliance needs, brand identity, and customer expectations.

Example: Bambu provides white-label robo advisory solutions for financial institutions worldwide.

Who it benefits: Banks, credit unions, and asset managers that want a fully branded solution without building everything from scratch.

With the rise of generative AI, these platforms can now deliver hyper-personalized investment recommendations and simulate multiple financial scenarios, giving clients a sense of human-like advice in real time.

In short, whether you want to complement existing advisors, offer a fully digital platform, or create a branded product, robo-advisors come in flexible forms that align with different strategies. Next, let’s talk about the benefits that Robo-advisor platforms offer to businesses across the globe.

Key Business Benefits of Robo Advisor Platform Development

Adopting a robo advisor platform is all about redefining how financial institutions deliver investment services while maintaining efficiency and client satisfaction. Here are the benefits that matter most to businesses:

1. Scalability Without Overhead

A robo advisor platform can serve thousands of clients at the same time without needing to expand advisory teams. Automation handles onboarding, risk profiling, and rebalancing, which helps firms grow faster while keeping costs predictable.

2. Hyper-Personalized Client Experiences

Algorithms adapt portfolios based on client behavior, risk tolerance, and financial goals. With advancements like generative AI agents, platforms can simulate multiple investment scenarios, offering investors tailored recommendations that feel as unique as human advice.

3. Lower Operational Costs

Traditional advisory models involve high labor and infrastructure expenses. Robo advisors automate compliance checks, reporting, and portfolio monitoring. For firms considering digital-first growth, it can be as impactful as a full-scale business app development using AI project that cuts inefficiencies across the board.

4. Smarter Decision-Making With AI

Beyond automating repetitive tasks, robo advisors integrate intelligent models that analyze data in real time. Over time, these systems refine strategies automatically, giving clients sharper, forward-looking insights without adding complexity for businesses.

For financial businesses, these benefits are not theoretical. They translate into higher profitability, better client retention, and stronger competitiveness. As expectations rise, knowing the must-have features of secure robo advisor apps becomes the next step in planning a successful platform.

Must-Have Features for Secure Robo Advisor Platform Development

The success of a robo advisor platform depends on the features it delivers. These features are not just about making the app functional. They ensure compliance, security, and client trust while creating a seamless experience that keeps investors engaged. Below are the essentials every robo advisor app should include:

Many financial institutions enhance these features further by embedding intelligence into them. For example, firms exploring build agentic AI can enable robo advisors to adapt autonomously, providing more proactive and human-like guidance.

These must-have features form the foundation of a robust platform. Once these essentials are in place, the next step is to explore advanced features that make robo advisor apps stand out in an increasingly competitive market.

Advanced Features That Make Robo Advisor App Development Stand Out

The essentials make a robo advisor platform functional, but advanced features give it a competitive edge. These are the capabilities that transform a standard investment tool into an intelligent, client-first solution.

1. Conversational AI for Client Engagement

Adding voice- or text-based assistants enables clients to interact with the platform naturally instead of just clicking through menus. With AI chatbots in banking and finance, robo advisors can handle onboarding, answer investment-related questions, and provide financial education in real time, all while lowering reliance on human advisors.

2. Smart Goal Tracking and Behavioral Nudges

Advanced systems actively monitor user behavior, suggest adjustments when goals go off track, and deliver gentle nudges to encourage better investing habits. This proactive guidance helps clients feel supported and more engaged with their financial journey.

3. AI-Driven Predictive Analytics

Beyond static reporting, AI can analyze massive datasets to forecast market movements and identify risks before they impact client portfolios. Firms adopting this feature elevate client confidence, making their robo advisor comparable to high-end wealth management software solutions that large institutions already trust for decision-making.

4. Multi-Asset Class Support

Investors today expect more than just ETFs and stock portfolios. Advanced robo advisors expand options by including bonds, commodities, real estate, and alternative assets, giving clients access to a more diversified investment strategy while still managing everything from one app.

5. Seamless Integrations With Core Banking Systems

For financial institutions, isolated platforms are not enough. Integrating robo advisors with custodians, payment gateways, and CRMs creates a streamlined ecosystem where transactions, compliance, and reporting all flow together smoothly.

6. Personalized Education Modules

Clients often need help understanding why a recommendation makes sense. Built-in learning modules explain strategies in plain language, using tutorials and simulations to simplify complex concepts. This feature builds trust and reduces support queries, while positioning the platform as both an advisor and a teacher.

Advanced features like these move robo advisors from being “just another financial app” to becoming a complete digital wealth partner. Next, let’s walk through the step-by-step process to develop a robo advisor platform for financial businesses.

Step-by-Step Process to Develop a Robo Advisor Platform for Financial Businesses

Building a robo advisor platform is not only about developing an app. It requires combining AI-driven intelligence with strong compliance, security, and user-first design. Below is a step-by-step process tailored specifically to robo advisor platform development.

1. Discovery and Planning

This stage begins with defining the type of robo advisor to build: hybrid, pure digital, or white-label. Businesses identify investor needs such as risk assessment, goal tracking, or automated rebalancing. Compliance frameworks like SEC and FINRA are also mapped out from the start to avoid setbacks.

• Estimated time:3–4 weeks

2. UI/UX Design

A robo advisor must feel trustworthy and intuitive for clients who may be skeptical about handing money to an algorithm. The design should emphasize simplicity in dashboards, clear portfolio views, and smooth onboarding. Partnering with a UI/UX design company ensures an interface that reduces friction and builds confidence.

• Estimated time:4–6 weeks

Also read: Top UI/UX design companies in USA

3. Core Engineering and MVP Development

Instead of building every feature upfront, businesses should launch with an MVP that includes account creation, KYC integration, and goal-based portfolio generation. This version delivers immediate value while allowing financial firms to gather real user feedback before scaling. Choosing the right MVP development partner ensures stability and compliance from the start.

• Estimated time:8–12 weeks

Also read: Custom MVP software development

4. AI and Data Integration

A robo advisor is only as good as its intelligence. AI models must be trained on diverse financial data to generate accurate risk scores and portfolio recommendations. Continuous learning loops help the system adapt to market conditions and client behavior over time, improving both accuracy and trust.

• Estimated time:6–8 weeks

5. Security, Compliance, and Testing

Robo advisors deal with highly sensitive financial data. Encryption protocols, role-based access, and regular penetration tests are essential. Compliance validation ensures the platform meets standards like GDPR, PCI DSS, and local financial regulations, giving clients peace of mind about data safety.

• Estimated time:4–6 weeks

Also Read: Software Testing Companies in USA

6. Deployment and Cloud Readiness

A successful robo advisor must be capable of handling fluctuating user traffic, especially during market volatility. Cloud infrastructure ensures uptime and responsiveness, while CI/CD pipelines allow new features like tax-loss harvesting or chatbot integration to roll out smoothly.

• Estimated time:3–4 weeks

7. Post-Launch and Continuous Optimization

Once live, the platform requires ongoing tuning. AI models need retraining with fresh data, while user analytics highlight which features drive engagement and retention. New modules like predictive market analysis or personalized financial education can be added to keep the platform ahead of competitors.

• Estimated time:Ongoing

Each step builds a solid foundation for delivering a secure, AI-powered robo advisor that meets investor expectations while staying compliant. With the process in place, the next focus is the recommended tech stack for robo advisor software development using AI.

Recommended Tech Stack for Robo Advisor Software Development Using AI

Choosing the right tech stack can determine whether your robo advisor platform becomes a secure, scalable solution or a costly bottleneck. The right combination of tools ensures smooth client experiences, fast performance, and long-term compliance with financial standards. Given below is a techstack that you can ideally consider for robo advisor platform development.

With the stack clarified, the next question every decision-maker asks is the cost of robo advisor platform development.

Cost of Robo Advisor Platform Development for Fintech Companies

The cost of developing a robo advisor platform typically falls between $35,000 and $300,000+, depending on scope, complexity, and compliance needs. These figures are ballpark estimates meant to guide planning rather than fixed quotes, since factors like AI sophistication, security layers, and integrations can significantly influence the budget.

Actual development costs can shift depending on the chosen tech stack, regulatory environment, and level of personalization required. For instance, projects incorporating advanced analytics and client personalization inspired by fintech in wealth management practices may trend toward the higher end.

Budgeting for robo advisor software is not just about development. It is about building a foundation that generates consistent returns. That is why understanding monetization models is just as critical as knowing the upfront investment.

Also Read: What is the cost of AI app development?

How to Monetize Robo Advisor Apps?

Knowing the development cost is only half the story. Profitability comes from designing monetization models that work for both financial institutions and their clients. A well-chosen revenue strategy ensures that robo advisor platforms not only sustain themselves but also scale effectively.

1. Assets Under Management (AUM) Fees

The most widely used model charges a percentage of assets under management. Since revenue grows as client portfolios grow, this model naturally aligns business incentives with investor outcomes.

2. Subscription Plans

Monthly or annual subscriptions create predictable income streams. Firms often combine tiered plans with feature upgrades, offering entry-level access at low cost and premium tiers with advanced AI features and deeper insights.

3. Freemium + Premium Features

Offering free basic features helps widen adoption, while premium add-ons like tax-loss harvesting or predictive analytics can be unlocked through paid plans. This balance lowers entry barriers while still driving revenue.

4. Institutional White-Labeling

Banks and credit unions often prefer branded robo advisors integrated into their existing digital systems. White-label licensing allows fintechs to sell the same core engine to multiple institutions, similar in approach to how on-demand app development solutions scale across different industries.

5. AI-Powered Add-Ons

Beyond the basics, businesses can create premium AI-driven modules such as risk simulators, predictive portfolio scoring, or conversational interfaces. Much like finance AI agent development, these extensions open new revenue streams while differentiating the platform.

Each of these models can stand alone or be combined for maximum impact. Once monetization is clear, the next step is to explore best practices for digital wealth management platform development that keep the system secure, compliant, and future-ready.

Best Practices for Robo-Advisor Platform Development

Robo advisor platforms deal with complex data, strict compliance, and high client expectations. To succeed, businesses must follow practices tailored to the financial services domain, not just general app development. Here are the key best practices to follow.

1. Embed Compliance Into Every Layer

From KYC during onboarding to SEC and FINRA requirements in reporting, compliance cannot be treated as an afterthought. By making it part of the design, robo advisors avoid costly rework and gain credibility with investors who demand transparency.

2. Engineer for Market Volatility

Unlike typical apps, robo advisors must stay stable even when thousands of users rebalance portfolios during sudden market shifts. A scalable backend ensures seamless performance during peak trading activity and prevents loss of client trust.

3. Use AI to Deliver Real Value, Not Just Automation

Predictive risk analysis, portfolio rebalancing, and goal-based recommendations are where AI shines. Applying intelligence in these areas improves decision-making and creates confidence. Our blog about the role of AI in wealth management shows how clients respond better when AI enhances clarity, not complexity.

4. Design for Investor Confidence

Clients want more than pretty dashboards. They want interfaces that simplify complex portfolio data, explain recommendations in plain language, and provide transparency into how decisions are made. Good UX here directly impacts adoption rates.

5. Plan for Continuous Feature Evolution

Robo advisors cannot remain static. Adding modules like tax-loss harvesting, ESG investing, or conversational interfaces keeps them relevant. Regular iteration ensures the platform adapts alongside client expectations and market innovation.

When these practices are followed, robo advisor platforms move beyond “functional apps” into trusted financial partners. The next step is tackling the challenges in robo advisor platform development and strategies to overcome them.

Challenges in Robo Advisor Platform Development and How to Overcome Them

Robo advisor platforms promise scalability and efficiency, but building one comes with hurdles that are unique to financial services. From handling sensitive client data to integrating AI models that make reliable recommendations, businesses need to plan for both technical and compliance obstacles. Here are the most common challenges and practical solutions.

These challenges are not roadblocks but areas that require strategic planning. By solving them, businesses can deliver platforms that inspire trust and deliver measurable results. The next step is to explore the future of robo advisor software development with AI and how it will shape financial services.

The Future of Robo Advisor Software Development With AI

Robo advisors are no longer just cost-saving tools. They are quickly evolving into full-fledged digital financial partners. The future lies in blending advanced AI, personalization, and compliance into platforms that don’t just recommend but anticipate investor needs. Here are the trends shaping the next decade.

1. Generative AI for Hyper-Personalization

Future robo advisors will move beyond static risk profiles and adapt to clients in real time. By using generative AI, platforms can simulate multiple portfolio scenarios, predict risks, and offer dynamic financial plans. This shift is already preparing generative AI in wealth management as a core driver of innovation.

2. Conversational Interfaces and Virtual Assistants

Investors expect instant answers, not long wait times. Future platforms will embed AI-driven conversational layers that explain investment decisions in plain language. Building AI chatbots for finance is already paving the way for robo advisors to become interactive financial coaches rather than silent calculators.

3. Seamless Multi-Device Experiences

Investors will expect the same consistent experience whether they are using a desktop, mobile app, or wearable device. Future robo advisors will sync portfolios across devices in real time, allowing users to monitor and act instantly. This continuity of access builds trust and increases engagement for always-connected clients.

4. Deeper Integration With Banking Ecosystems

Robo advisors will no longer operate as standalone platforms but as part of broader digital banking suites. They will integrate with savings accounts, lending products, and even credit management tools. This convergence creates one-stop ecosystems that make financial management seamless and highly convenient.

5. Stronger Emphasis on Explainable AI (XAI)

Transparency will be non-negotiable as regulations tighten. Robo advisors will need to justify every recommendation with clear explanations, giving clients both confidence and control over their financial decisions. This will also help firms demonstrate compliance during audits and build stronger long-term trust.

The future of robo advisors is intelligent, transparent, and deeply personalized. With these innovations on the horizon, businesses must decide who can help them bring their vision to life.

Why Choose Biz4Group to Develop Your Robo Advisor Platform?

Building a robo advisor platform requires a partner who understands how to blend AI, compliance, and user trust into one seamless experience. At Biz4Group, we specialize in delivering intelligent financial applications that set the foundation for scalable and investor-ready platforms.

• WorthAdvisors


WorthAdvisors is a digital platform that empowers financial advisors to create tailored wealth strategies for clients. The project highlights our ability to translate complex investment processes into simple and intuitive digital journeys. This is exactly what robo advisors must achieve to gain client confidence.

• AI-Powered Chatbot for Human-like Communication

We developed an advanced AI chatbot capable of delivering contextual, human-like conversations at scale. For robo advisor platforms, this expertise directly supports features such as investor Q&A, onboarding assistance, and real-time portfolio explanations that improve transparency and adoption.

As a leading AI app development company, Biz4Group combines AI-first thinking with financial domain expertise. Whether it is integrating predictive analytics, ensuring regulatory compliance, or designing investor-friendly dashboards, we know how to deliver robo advisor platforms that win client trust.

Conclusion: Time to Create Your Robo Advisor Application

If there’s one thing this guide proves, it’s that robo advisor platforms are the future of how investors interact with financial services. From onboarding clients with ease to serving up AI-powered portfolio insights, these platforms combine trust, transparency, and technology in ways traditional advisory models simply cannot match.

The best part? You don’t have to tackle this journey alone. With the right mix of AI consulting services and AI product development services, your business can skip the trial-and-error phase and move straight to launching a compliant, scalable, and investor-ready robo advisor platform.

The question is not whether the market is ready. It already is. The only real question is: will it be your platform that investors choose first?

Investors are ready for smarter advice. Let’s craft your robo advisor platform together.

FAQs

1. How long does it take to develop a robo advisor platform?

Development timelines depend on scope and features. An MVP version may take 4–6 months, while a fully featured enterprise-grade platform could extend to 9–12 months or more.

2. What technologies are most important for building a robo advisor platform?

AI and machine learning frameworks, secure cloud infrastructure, and user-centric front-end frameworks are essential. These technologies ensure scalability, compliance, and accurate financial recommendations.

3. What is the cost of developing a robo advisor platform?

The cost typically ranges between $35,000 and $300,000+ depending on whether you build an MVP, a mid-level platform, or a full enterprise solution. These are ballpark figures, and actual costs vary based on complexity.

4. How do robo advisor platforms ensure data security?

They implement multi-factor authentication, encryption, and compliance with global financial standards such as GDPR and FINRA. Regular audits and penetration testing also play a role in safeguarding sensitive client data.

5. Can robo advisor platforms support hybrid models with human advisors?

Yes. Many successful platforms combine automated portfolio management with access to human advisors. This hybrid approach balances efficiency with personal interaction, making it appealing to both digital-first and traditional clients.

6. What future trends should businesses consider before developing a robo advisor platform?

Trends include the use of generative AI for hyper-personalization, conversational interfaces for improved engagement, and deeper integration with banking ecosystems. These shifts will define the next wave of investor expectations.

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