DEV Community: Deepak Arora

Securing the 5G Future with Intelligent SMS Firewalls as a Growth Driver

Deepak Arora — Tue, 23 Sep 2025 10:20:21 +0000

5G is not just another step in mobile network evolution. It marks an important inflection point for the telecommunications industry, transforming a basic network upgrade into a complete architectural overhaul. While this overhaul creates opportunities for telecom operators and the adoption of Private 5G solutions, it also introduces serious security challenges. As 5G transforms connectivity, it simultaneously changes the nature and scale of threats. This is a serious concern because SMS messaging remains a cornerstone of telecom revenue.

The Revolution with Unique Opportunities and Complexities

5G’s Transformative Capabilities

5G delivers up to 10 Gbps speeds, ultra-low latency of just 1 millisecond, and the capacity for one million devices per square kilometer –100 times more than 4G. Ericsson’s 2025 Mobility Report projects global 5G subscriptions are projected to reach 5.6 billion by 2029. Unlike 4G, 5G is built on virtualization, cloud-native functions, and software-defined components. This enables operators to scale dynamically, launch new features faster, and tailor offerings for diverse industries.

The Dark Side: Expanded Attack Surface

5G offers tremendous potential through software-driven networks, edge computing, and massive device connectivity. However, it also brings unprecedented security complexity. The shift from hardware-centric to software-driven networks has exponentially expanded the attack surface. Studies show a 200% increase in threat exposure tied to 5G’s increased architectural complexity.

Unlike 4G networks, where most threats were external and perimeter-focused, 5G vulnerabilities often emerge from within the network itself. Threats now arise from internal misconfigurations, compromised cloud APIs, vulnerable partner integrations, or cross-protocol exploits. As a result, traditional perimeter-based security is no longer sufficient.

New Threat Vectors in the 5G Era

Virtualization-Driven Vulnerabilities

Network Slicing Risks: Network slicing allows operators to create isolated virtual networks for specific services. However, these slices often share the same physical infrastructure. A security breach in one slice can cascade across others, compromising the entire network.

Cloud Misconfigurations: 5G’s heavy reliance on virtualized infrastructure and cloud services introduces new vulnerabilities. According to IBM’s Cost of a Data Breach Report 2025, 30% of breaches involved data stored across public clouds, private clouds, and on-premises. A decade ago, cloud misconfigurations weren’t even classified as threats; today, the cloud and its data are prime targets.

Edge Computing Exposure: The decentralized nature of edge computing brings processing closer to users but dramatically increases the number of potential attack points. With billions of IoT devices connecting through 5G, operators face risks from subtle side-channel attacks that exploit signals like power consumption or electromagnetic timing.

SMS-Specific Fraud Evolution

Massive Traffic Exploitation: According to Juniper Research, A2P messaging is on the rise, with the SMS traffic set to reach 3.4 trillion in 2028. Coupled with 5G’s high-speed connectivity, this creates opportunities for fraudsters to flood networks with spam, manipulate billing, or launch sophisticated smishing campaigns.

Protocol Abuse: Fraudsters may exploit 5G’s network slicing to create isolated environments that bypass traditional monitoring systems. Such misuse of new 5G protocols and services could leave operators increasingly vulnerable to frauds.

Grey Route Exploitation: Most concerning is the evolution of grey routes—unauthorized pathways for A2P SMS delivery. Mobilesquared’s Global A2P SMS Report 2025 estimates that total international grey route traffic was 94.5 billion messages, accounting for 31.8% of all international traffic. Fraudsters can now exploit 5G’s ultra-low latency at the edge to make these unauthorized routes even more efficient, bypassing legacy detection systems.

The Imperative for Next-Generation SMS Firewalls

Traditional SMS firewalls are ill-equipped to handle 5G’s complexity. To be truly 5G-ready, an SMS firewall must include the following critical capabilities:

Deep Packet Inspection and Application Awareness: Next-gen firewalls must go beyond basic packet filtering and analyze application-layer data. By understanding SMS content and context, they can more effectively identify threats.

Multi-Protocol Integration: SMS firewalls should integrate with signaling firewalls across SS7, Diameter, SIP, and GTP. This approach ensures unified protection against threats that may exploit vulnerabilities across protocol layers.

Cloud-Native Architecture: Containerized, cloud-native deployment enables scalable, flexible protection across core, edge, and cloud environments.

AI/ML-Driven Analysis: Modern SMS firewalls must leverage AI and ML to analyze traffic patterns in real-time, detect anomalies and predict threats with greater accuracy and speed.

Adaptive Threat Intelligence: Continuous updates from global threat intelligence feeds help firewalls stay ahead of rapidly evolving attack tactics.

Real-Time URL/Content Filtering: Blocks malicious URLs and embedded content in SMS, protecting subscribers from phishing and malware.

Secure Key Management (SKM) and Isolation: Provides secure handling of cryptographic keys while isolating sensitive traffic to prevent unauthorized access.

Advanced Analytics and Reporting: Delivers actionable insights into traffic patterns, fraud attempts, and compliance metrics.

Strategic Business Impact

The implementation of advanced SMS firewalls delivers measurable business outcomes that go far beyond basic security:

Revenue Protection and Growth

The financial impact of next-generation SMS firewalls is substantial. According to Mobile Ecosystem Forum, operators deploying advanced firewall solutions recovered 413% more A2P revenue in the first year, and up to 1,425% by year two. This dramatic improvement comes from blocking grey route traffic and ensuring legitimate A2P messages reach their destinations.

Enhanced Customer Trust

Advanced SMS firewalls boost customer satisfaction by reducing spam messages and smishing attempts. This reduces churn and leads to higher customer lifetime value.

Regulatory Compliance

With automated compliance reporting, intelligent cloud SMS firewalls ensure adherence to GDPR and SMS traceability mandates. This helps operators avoid penalties and stay aligned with evolving global standards.

Enterprise Monetization

Secure, reliable A2P messaging enables operators to pursue enterprise markets, such as banking, e-commerce, and government services. Operators with advanced firewalls stand to prevent over $4.4 billion in lost business messaging revenue between 2023 and 2027.

Best Practices for 5G SMS Security

Security Maturity Assessment

Operators should benchmark their current capabilities against a structured maturity model:

Basic/Legacy Stage: Perimeter firewalls with static rules, limited visibility, and a reactive security posture
Intermediate Stage: Hybrid-cloud architectures with basic automation and partial protocol integration
Advanced Stage: Fully cloud-native, AI-driven systems with full protocol coverage and unified compliance reporting

Implementation Strategy

Successful 5G SMS security requires:

Continuous Modernization: Operators should adopt cloud-managed SMS firewalls and automation to strengthen security operations, improve scalability, and counter evolving threats.

Integrated Security Posture: Combining SMS and signaling firewalls provides end-to-end protection across control, user, and application layers. Security controls must span the core, edge, and cloud to address 5G’s distributed architecture.

Real-Time Adaptation: Adaptive analytics and threat intelligence sharing initiatives help operators stay ahead of evolving fraud tactics.

Stakeholder Collaboration: Cross-functional threat response teams and vendor SLAs for fraud resolution enhance security outcomes. Clear timelines and responsibilities ensure rapid response to threats.

Conclusion

The 5G era brings both unique opportunities and equally significant security challenges. Only adaptive, cloud SMS firewalls can keep pace with this new reality, protecting networks, customers, and revenue streams while enabling the full potential of 5G.

The question remains: how quickly can operators implement modern firewall solutions to match 5G’s sophistication? Those who act decisively will thrive; those who hesitate risk falling behind.

Smart Lending – How Decision Engines are Shaping the Future of LOMS

Deepak Arora — Wed, 18 Jun 2025 08:10:22 +0000

In today’s world, where everything happens at the click of a button, consumers expect loans to be digital, quick, convenient, and personalized. The global loan origination and management market is booming, reflecting this growing demand. It’s expected to reach $3.3 billion (source: spherical insights) by 2030, fueled by the desire for efficient and user-friendly lending solutions.

Loan origination and management systems (LOMS) have emerged as indispensable tools, streamlining the entire loan lifecycle from application to disbursement and transforming how lending is done.

Loan origination system (LOS)

The LOS streamlines the loan application and approval process, from initial submission to final decision. It automates tasks like data collection, credit scoring, and document verification, making the process faster and more efficient for both lenders and borrowers.

Loan management system (LMS)

The loan management system manages loans after they have been originated. It helps lenders track loan performance, manage collections, ensure compliance, and generate reports for analysis. By automating these tasks, LMS improves overall loan portfolio efficiency and profitability.

The future of digital lending lies in comprehensive loan origination and management systems (LOMS) that leverage decision engines to automate and optimize various processes. Decision-based engines play a crucial role in automating and optimizing various processes, from loan application assessment to risk evaluation and post-disbursement management.

By leveraging data-driven insights and advanced algorithms, these engines enable lenders to make more accurate and efficient decisions, ultimately improves customer satisfaction, reduces operational costs, and drives sustainable growth.

Understanding decision engines

Decision engines are the essential components of digital LOMS. They are designed to automate the decision-making process, streamline loan applications, and ensure consistent, efficient outcomes.

By leveraging advanced technologies, these engines evaluate:

Credit score and history: Analyzes the borrower’s past credit behavior and current credit score to predict future repayment.

Income and employment status: Verifies the borrower’s income levels and employment situation to assess their ability to repay.

Debt-to-income ratio: Evaluates the proportion of the borrower’s monthly income that goes towards debt payments, helps to determine their capacity for additional debt.

Assets and collateral: Assesses the borrower’s assets that can serve as collateral, provides security for the loan.

Economic conditions: Considers prevailing economic factors such as interest rates and market trends that could impact the borrower’s ability to repay.

Alternative data sources: Utilizes non-traditional data such as utility payments, rental history, and social media activity to assess borrowers without extensive credit histories.

Fraud detection: Employs algorithms to identify potentially fraudulent activities by analyzing patterns in real-time data.

Regulatory compliance: Ensures that all lending decisions comply with relevant legal and regulatory requirements, including data protection laws.

Risk assessment models: Uses predictive analytics and machine learning models to assign risk scores based on various financial.

Personalized credit offers: Tailors loan offers to individual borrowers based on their unique financial profiles and needs, enhancing customer satisfaction.

Open banking data: Incorporates data shared through open banking platforms for a more comprehensive view of the borrower’s financial situation.

Building blocks of decision engines

Decision engines rely on a powerful combination of data, rules, algorithms, and more to make informed decisions in LOMS. Here is a quick look at them:

Data intake engine: Integrates data from multiple sources, including third-party data providers, to gather essential information such as credit scores, employment verification, and financial statements. It ensures that all relevant data is available for analysis.

Core decision and scoring engine: Uses algorithms and scoring models to evaluate creditworthiness based on various criteria like credit history, income, and debt-to-income ratio. It calculates risk scores and determines eligibility for loan products.

Business rules engine: Applies predefined rules and logic to the data processed by the decision engine. It uses if-then statements and complex conditions to automate decision-making processes, ensuring that decisions are consistent with the institution’s policies.

Algorithms: Processes data and applies rules using mathematical models. They use statistical techniques and machine learning to analyze data, identify patterns, and predict outcomes. Common algorithms used in decision engines include regression analysis, decision trees, and neural networks.

Predictive analytics and machine learning models: Utilizes historical data to predict future behaviors, such as the likelihood of default. They help in assessing risk more accurately and tailoring loan products to meet specific customer needs.

Workflow automation: Automates various stages of the loan origination process, routes applications through different stages based on predefined criteria. It reduces manual intervention and speeds up the approval process.

Enterprise system integration: Integrates with other enterprise systems to facilitate seamless data flow and application processing. This integration supports actions such as identity verification and document collection.

Regulatory compliance framework: Ensures that all decisions comply with legal and regulatory standards, adapts to changes in regulations as needed.

Alternative data sources: Incorporates non-traditional data such as social media activity, utility payments, and rental history to assess borrowers without extensive credit histories.

Open banking integration: Allows customers to share their financial data with third-party providers for better loan terms and rates, enhances personalization of credit offers.

Key benefits of using decision engines in LOMS

Decision engines equipped with advanced analytics have emerged as a game changer in the lending industry. As lenders are constantly looking for innovative solutions to improve their operations and make more informed decisions, decision engines rise up to the occasion, providing lenders several benefits:

Increased speed and efficiency: Automates the loan origination process, significantly reduces the time required to process applications. This leads to faster approvals and disbursements, enhances customer satisfaction and gives lenders a competitive edge.

Improved accuracy and consistency: Utilizes advanced algorithms and data analytics, decision engines minimize human errors and ensure consistent application of lending criteria across all decisions. This uniformity helps mitigate biases and enhances the reliability of credit assessments.

Cost reduction: Automates the decision-making process and reduces operational costs by decreasing the need for extensive manual processing and IT support. This cost-effectiveness is especially beneficial in managing varying application volumes without additional resources.

Enhanced risk management: Incorporates predictive analytics and machine learning to assess risks more accurately, allows lenders to tailor loan products to specific customer needs that minimize default risks.

Regulatory compliance: Ensures all decisions comply with internal policies and external regulatory standards, provides full transparency and traceability of decision-making processes.

Scalability: Handles large volumes of applications efficiently, makes solutions that adapt to changing market conditions without compromising service quality.

Fraud detection: Identifies indicative patterns of fraudulent activity by analyzing vast amounts of data in real-time, thereby safeguards both lenders and borrowers.

Enhanced customer experience: Provides quick responses and reduces waiting times, decision engines improve customer satisfaction. They also allow for personalized loan offers based on individual financial profiles.

Flexibility and adaptability: Adjusts decision rules and strategies for business users in response to market changes without complex coding, ensures that institutions remain agile.

Key use cases of decision engines in LOMS

Decision engines offer a wide range of applications that help lenders assess the borrower as well as the loan across different parameters. These powerful tools empower lenders to make more informed decisions, streamline operations, and enhance customer experiences. Here are some key use cases:

Automated credit evaluation: Automate the assessment of credit applications by integrating with various data sources to evaluate credit scores, income, employment status, and other financial indicators. This automation speeds up the decision-making process and reduces manual errors.

Underwriting process: Underwrite process by automatically retrieving and analyzing necessary documents such as bank statements, tax returns, and employment verification. This ensures a thorough evaluation of the borrower’s creditworthiness.

Risk assessment and management: Use predictive analytics and machine learning models to assess risk factors associated with lending. They provide insights into potential default risks, allowing lenders to make informed decisions.

Regulatory compliance: Ensure that lending decisions comply with relevant legal and regulatory standards by executing predefined business rules that align with current regulations.

Fraud detection: Analyze patterns in application data, decision engines can identify potentially fraudulent activities, thereby protecting both lenders and borrowers from fraud risks.

Personalized loan offers: Enable lenders to tailor loan products to individual borrower profiles by evaluating comprehensive data sets. This personalization improves customer satisfaction by offering terms that best fit the borrower’s needs.

Workflow automation: Streamline various stages of the loan origination process, from initial application to final approval, reduces processing times and improves operational efficiency.

Real-time decision making: Provide real-time assessments and decisions on loan applications, enhances the speed and responsiveness of financial institutions in meeting borrower demands.

Integration with third-party data providers: Integrate with external data sources via APIs to validate application data, ensures that all necessary information is accurate and up to date.

Continuous monitoring and loan management: Monitor loans throughout their lifecycle for compliance and performance management, ensure ongoing risk assessment and management.

Challenges and considerations

Decision engines, while offering significant benefits, present challenges that need to be considered. Hence it becomes important for lenders to understand and carefully consider them to successfully leverage decision engines and maximize their benefits.

Data quality and integrity: The effectiveness of decision engines heavily relies on the quality and integrity of the data they process. Inaccurate, incomplete, or biased data can lead to flawed assessments and decisions, necessitating robust data governance practices to maintain data quality.

Interpretability and explainability: Decision engines often use complex algorithms, making it difficult to interpret and explain the rationale behind certain credit decisions. This lack of transparency can pose significant challenges, especially in regulatory environments where explanations for decisions are required.

Bias and ethical concerns: AI models used in decision engines can inadvertently perpetuate biases present in historical data, leading to unfair or discriminatory lending practices. Ensuring fairness and preventing discrimination requires continuous monitoring and adjustment of these models.

Regulatory compliance: Adapting decision engines to comply with evolving regulatory requirements is a continuous challenge. Financial institutions must ensure that their systems are aligned with current regulations and can adapt to future changes.

Integration with legacy systems: Many financial institutions still rely on legacy systems that may not be compatible with modern decision engines. Integrating these engines into existing infrastructures can be complex and costly.

Cybersecurity risks: As decision engines process sensitive customer data, they are vulnerable to cybersecurity threats. Ensuring robust security measures are in place is crucial to protect data integrity and confidentiality.

Continuous monitoring and maintenance: Decision engines require ongoing monitoring and maintenance to remain effective. This includes updating algorithms, retraining models to prevent data drift, and ensuring systems are responsive to market changes.

Balancing automation with human oversight: While automation increases efficiency, maintaining an appropriate level of human oversight is necessary to ensure loan quality and compliance. Striking this balance can be challenging for financial institutions.

To address these challenges, financial institutions must invest in data quality initiatives, establish ethical guidelines, and seek expert guidance for implementation, to ensure effective use of decision-making engines.

Current trends and future outlook

As technology continues to advance, we can expect to see even more innovative applications of decision engines in LOMS. Some potential future trends include:

AI-driven predictive analytics: Leveraging artificial intelligence to enhance predictive analytics capabilities, decision engines can provide more accurate risk assessments by analyzing vast datasets, including non-traditional data sources like social media and mobile phone usage.

Real-time loan adjustments: Implementing real-time data analysis to dynamically adjust loan terms, interest rates, and repayment schedules based on the borrower’s current financial situation and market conditions.

Blockchain integration: Utilizing blockchain technology to enhance security, transparency, and efficiency in the loan origination process. Blockchain can facilitate secure data sharing and streamline verification processes.

Advanced simulations and what-if analyses: Enabling lenders to perform advanced simulations to test different scenarios and strategies, allowing for better risk management and decision-making.

Natural language processing (NLP) for document analysis: Using NLP tools to automate the extraction of meaningful information from unstructured data such as business reports and verbal communications, providing a more comprehensive view of a borrower’s financial health.

Open banking and API integration: Expanding the use of open banking platforms to allow borrowers to share their financial data with third-party providers for more personalized loan offers and better.

Enhanced fraud detection with AI: Employing AI to improve fraud detection by identifying unusual patterns or anomalies in application data that might indicate fraudulent activity.

Personalized customer interactions: Using AI-driven chatbots and virtual assistants to offer personalized advice and support based on the customer’s specific financial behavior and needs, enhancing customer experience.

Low-code/no-code solutions for rapid deployment: Implementing low-code or no-code platforms to enable rapid customization and deployment of decision engines without extensive IT involvement, increasing agility and responsiveness to market changes.

Conclusion

Decision engines are transforming the lending industry by automating and optimizing various processes from loan application assessment to risk evaluation and post-disbursement management, providing intelligent and efficient solutions. By leveraging decision engines, banks and financial institutions leverage data-driven insights and advanced algorithms to make accurate decisions and enhance operations.

As technology continues to evolve, we can expect to see even more applications of decision engines in the domain loan origination and management system driving innovation and improving customer experiences.

How to choose the right digital wallet platform provider for your business?

Deepak Arora — Mon, 09 Jun 2025 11:32:20 +0000

In contrast, digital wallet solutions offer businesses a centralized platform to securely store payment methods, loyalty cards, and important documents. This not only mitigates the risk of loss or theft but also simplifies the payment process for both customers and employees. With a digital wallet, businesses can seamlessly manage expenses, track transactions, and access vital information with just a few taps on a smartphone or other digital device.

This digital wallet era equips businesses with the tools they need to adapt to the evolving landscape of commerce. By integrating digital wallet solutions into their operations, businesses can enhance efficiency, reduce costs, and deliver superior service to their customers. It's time for businesses to bid farewell to the limitations of physical wallets and embrace the convenience and security of digital payment platforms.

In the last four years in Bangladesh, digital wallets have become twice as important in reshaping financial transactions, growing from 0.23 to 0.46 billion transactions. This surge is evident as banks, digital banking platforms, and digital wallet platforms experience a rise in digital remittances.

What is a Digital Wallet?

A digital wallet is a secure, convenient tool designed to enhance your financial transactions and safeguard your assets. Utilizing advanced features such as fingerprint or facial recognition, robust encryption, and multi-factor authentication, digital wallets prioritize your customer’s financial safety. With the ability to store funds, facilitate transfers, and link to your bank account, they streamline money management processes with effortless ease.

It represents an evolution of the conventional wallet, now accessible directly from your mobile device. As society increasingly embraces cashless transactions, digital wallets emerge as indispensable companions, facilitating seamless financial interactions and embodying a shift towards a more digitally integrated lifestyle.

The Evolution of Digital Wallet Solutions

Digital wallet solutions providers have undergone a remarkable transformation. Let's explore the pivotal moments that shaped them, starting from their beginnings in the tech boom to their current role.

Digital wallet platforms emerged with the rise of smartphones, as tech enthusiasts seized the opportunity to modernize traditional payment methods
Evolving, digital wallets now offer heightened convenience by integrating multiple features. Users make payments and store loyalty cards, boarding passes, and other essentials in one app.
The integration of Near Field Communication (NFC) was a game-changer, enabling seamless contactless payments with a simple smartphone tap, revolutionizing transaction speed and ease.
Today's digital wallet platforms have evolved into versatile financial management tools. You can effortlessly handle budgets, track expenses, invest, and access various financial services, seamlessly integrating these apps into your daily routines.

Advantages of Digital Wallet Solutions

Digital wallets are gaining widespread popularity due to their convenience and flexibility, This surge is not only attributed to the ease of transactions but also extends to the integration of digital banking platforms.

As digital wallets take center stage in managing financial activities, they offer users a comprehensive tool for everyday financial tasks. The increasing trend of people storing their card information on phones and wearables further underscores the growing significance of these digital platforms. A bunch of reasons that contribute to the above statement are:

Streamlined Convenience: Digital wallets simplify payment handling by consolidating various methods and eliminating the need for physical wallets.

Rise of Contactless Payments: Digital wallets, using NFC tech, are pushing the world towards safer, touch-free transactions.

Security Enhancement: Digital wallets take security seriously, using biometrics, PIN codes, and encryption to protect payment info. This layered approach minimizes risks like unauthorized access and fraud.

Financial Inclusion Drive: Digital wallets are key players in promoting financial inclusion. They bring digital financial services to underserved populations, allowing them to handle digital payments, money transfers, and basic financial tasks through smartphones.

Global Reach and Currency Flexibility: Digital wallets break boundaries, facilitating global transactions, currency conversions, and cross-border transfers.

Gateway to Innovation: Digital wallet platforms position businesses and individuals at the forefront of technological innovation, preparing them for future advancements in the digital landscape.

Key Consideration while Choosing a Digital Wallet Solution

Jumping on the cashless wave, especially with the help of digital wallet platform, is a game-changer in today's world

Selecting a digital wallet solution requires prioritizing the following key elements while also emphasizing the advantages discussed above.

Security and Privacy: You should opt for a provider with robust encryption, multi-factor authentication, and fraud prevention mechanisms to safeguard your financial assets and personal information.
Authentication Methods: Ensure the digital wallet offers you a variety of secure authentication methods, such as biometrics, PINs, and 2FA, enhancing user identity verification.
Compliance and Data Regulations: Choose a provider that adheres to GDPR, PCI DSS, AML Regulations, and Cybersecurity Regulations, demonstrating a commitment to your user data security.
Payment Options and Integrations: Look for a digital wallet that supports various payment methods, integrates with popular payment gateways, and facilitates peer-to-peer payments, ensuring versatility and convenience.
User Experience and Interface: Prioritise wallets with intuitive designs, seamless navigation, and customization features for a user-friendly experience, promoting your satisfaction and adoption.
Platform Capability: Opt for a provider offering compatibility across multiple operating systems and devices, maximising reach and ensuring accessibility for a diverse user base.
Loyalty Programs and Rewards: Choose a digital wallet that integrates loyalty programs and incentives, enhancing user engagement, fostering loyalty, and providing added value for regular users.
Customer Support and Reliability: Prioritise providers with responsive 24/7 customer support, quick issue resolution, and dispute handling capabilities, ensuring a positive user experience and building trust for you.
Cost and Pricing Transparency: Look for transparent fee structures and clear comparisons with similar solutions to help you make informed financial decisions, promoting trust and user satisfaction.
Scalability and Future-Proofing: Choose a provider that prioritises flexibility, upgradability, and adaptability to help you accommodate growth, technological advancements, and changing business needs.

Conclusion

As you evaluate digital wallet solution providers, consider how their dedication to research, client relationships, transparency, and support aligns with your business goals. The journey to finding the ideal digital wallet solution is an important one, and by selecting the best digital wallet platform provider, you can be confident that you're making a decision that resonates with innovation, reliability, and excellence.