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By the end of 2025, at least 40% of the global population will be subject to at least one IoB application, according to Gartner predictions. The Internet of Behaviors market was valued at $456.50 billion globally in 2024 and is projected to skyrocket to $3,804.80 billion by 2034 – representing a staggering 23.62% compound annual growth rate that highlights the explosive adoption of IoB in real-world applications.
For a clear distinction, IoB (Internet of Behaviors) is based on understanding, predicting, and affecting human behavior through data analysis. While IoT (Internet of Things) describes the network of physical objects embedded with sensors and software for connecting and exchanging data over the internet.
The Banking, Financial Services, and Insurance (BFSI) sector commands 24% of the global IoB market share, making it the leading adopter of this transformative technology. Let's dive deeper into IoB and explore how AI powers its revolutionary applications in finance.
What is IoB and How Does AI Power It?
IoB was first identified by Gartner in 2021 as an emerging technology that makes sense of data acquired by IoT devices in combination with specific human behaviors. Unlike IoT, which simply collects data, IoB interprets this information to understand and influence behavioral patterns.
AI serves as the brain behind IoB, transforming raw behavioral data into actionable insights. Machine learning algorithms analyze vast datasets from multiple sources: transaction patterns, device usage, location data, spending habits, and digital interactions. These AI systems use both supervised learning (trained on known patterns) and unsupervised learning (discovering new behavioral anomalies) to create comprehensive behavioral profiles.
Real-time processing is where AI truly shines. Modern IoB systems can analyze billions of data points instantly, identifying patterns that would be impossible for humans to detect. For example, bunq, a Netherlands-based neobank serving over 17 million users across the EU, uses NVIDIA-accelerated AI to boost fraud detection workflows, accelerating model training by 100x and data processing by 5x.
The integration extends beyond simple data collection. AI-powered IoB platforms like Sardine, which operates in over 70 countries, combine device intelligence, behavioral biometrics, and machine learning to create sophisticated risk assessment models that adapt in real-time to emerging threats.
what about IoB applications in Banking & Finance ?, is it feasible to use IoB ?
Key IoB Applications in Banking & Finance
Fraud Detection & Prevention
IoB excels at understanding patterns and detecting fraud in banking systems through behavioral pattern recognition. AI analyzes customer behavior to detect unusual activity with unprecedented accuracy. For example, if John typically makes regular deposits of $1,000 per week from freelancing but suddenly receives multiple $1,000 deposits every two days from different sources, AI systems can instantly flag this deviation for investigation.
Global implementations are already showing remarkable results:
Standard Bank Group, Africa's largest bank by assets, has implemented Swift's AI-powered fraud detection service, which identifies suspicious patterns in real-time to reduce fraud risk and ensure safer banking experiences.
Royal Bank of Scotland prevented losses of over $9 million to customers using AI analytics for behavioral fraud detection.
EBA CLEARING launched a pan-European fraud detection pilot (FPAD) with nine banks across six countries, covering real-time fraud prevention tools including behavioral analytics.
Personalized Financial Services
IoB enables highly customized financial products and services by analyzing behavioral data for dynamic credit risk assessment. Instead of relying solely on traditional credit scores, banks can now evaluate how customers interact with their devices, their spending patterns, and transaction behaviors to create more accurate risk profiles.
Credit scoring revolution: AI analyzes behavioral data to provide nuanced credit assessments. A customer who consistently pays bills on time, maintains stable spending patterns, and shows responsible digital banking behavior might receive better rates, even with a limited credit history.
Product recommendations: Behavioral insights drive targeted financial advice customized to each client. If IoB detects that a customer frequently travels internationally, the system might recommend travel-friendly credit cards or foreign exchange services.
Customer Experience Enhancement
AI excels at enhancing user engagement through omnichannel personalization, determining customers' preferred channels for tailored messaging. Banks can predict customer needs before they arise based on historical behavioral data.
A notable example is Orange's partnership with Intent HQ in Spain, which developed the 'Market Explorer' service – an analytics solution using Big Data and AI technologies that offers detailed information on real consumer behavior for personalized banking services.
Proactive support represents the future of customer service. If IoB detects unusual account activity patterns that typically precede customer service calls, banks can proactively reach out with solutions before customers even realize they need help.
4. The Technology Behind IoB in Banking
Let's make it simple, IoB is a combination of sophisticated technologies working together together to output human's behaviours in a readable way for human :
Machine Learning Models form the foundation, using both supervised and unsupervised learning for pattern recognition. These models continuously learn from new data, adapting to evolving customer behaviors and emerging fraud tactics.
Real-time Processing Systems handle billions of records to identify complex fraud patterns instantly. Swift's network, connecting over 11,500 banking organizations in more than 200 countries, processes massive volumes of transaction data to detect suspicious patterns across global payment flows.
Behavioral Analytics Platforms provide the specialized tools needed for IoB implementation:
- Featurespace's ARIC platform offers adaptive behavioral analytics for anomaly detection
- Feedzai's AI-native platform provides global financial crime prevention capabilities
- Temenos uses NVIDIA NIM microservices to deploy generative AI models for credit scoring, fraud detection, and customer service across banks worldwide
Agentic AI represents the cutting edge, creating autonomous agents for KYC/AML automation and investigation. These systems can make decisions independently while maintaining human oversight, dramatically improving efficiency in compliance processes.
Integration challenges remain significant, as banks must connect IoT devices, transaction systems, and behavioral data across multiple platforms while maintaining security and privacy standards.
Let's discover it's benefits and challenges.
Benefits & Challenges
Companies like Standard Bank, bunq, and Swift are investing heavily in IoB because the benefits are substantial:
Benefits:
Accuracy: AI proves more effective at fraud detection than manual controls. Studies show that AI systems can reduce false alerts by up to 50% while catching more actual fraud.
Cost Reduction: Automated behavioral analysis reduces manual oversight significantly. Banks can now automate routine investigations and focus human resources on complex cases that require judgment.
Customer Satisfaction: Fewer false positives mean smoother experiences for legitimate customers. When fraud detection systems are more accurate, customers face fewer declined transactions and account freezes.
Competitive Advantage: Enhanced customer experiences and superior risk navigation provide banks with differentiation in crowded markets. Institutions using IoB can offer more personalized services while maintaining stronger security.
Challenges:
Privacy Concerns: Large-scale behavioral tracking raises significant ethical questions. With 63% of people finding it unsettling that IoT devices constantly collect their data, banks must balance insight generation with privacy protection.
Regulatory Compliance: The need for transparency and data protection compliance varies across regions. European banks face GDPR requirements, while other regions have different privacy frameworks, creating complex compliance landscapes.
False Positives: Balancing security with customer experience remains challenging. Even AI systems can misinterpret legitimate unusual behavior as suspicious, potentially impacting customer relationships.
Bias Risks: Ensuring fairness and non-discrimination in AI models requires constant monitoring. Behavioral patterns might inadvertently discriminate against certain demographic groups or cultural behaviors.
6. Future Outlook & Implications
The IoB market shows no signs of slowing, with 23%+ CAGR expecd through 2034 globally. Regional trends reveal fascinating adoption patterns:
Asia-Pacific emerges as the fastest-growing region, driven by smart city initiatives in India andina's leadership in cloud spending (reaching $20 billion in 2020). These investments in digital infrastructure create ideal conditions for IoB implementation.
Europe advances through regulatory frameworks and collaborative initiatives. e continent's focus on privacy-first innovation and cross-border cooperation positions it as a leader in responsible IoB deployment.
Africa shows growing adoption through major banks like Standard Bank Group, demtrating how IoB can leapfrog traditional banking infrastructure limitations in emerging markets.
Technology Evolution sees generative AI adoption moving from experimental pilots to strategic enterprise implementation. Banks are increasingly integrating IoB into core business processes rather than treating it as a supplementary tool.
Regulatory landscapes worldwide are evolving to address IoB challenges. We expect comprehensive privacy laws and ethical AI guidelines to emerge, creating standardized frameworks for responsible IoB implementation.
Industry transformation will fundamentally reshape banking relatioships. The future bank will know customers' needs before they do, predict financial stress before it occurs, and provide proactive solutions that enhance financial well-being.
7. Conclusion
The Internet of Behaviors represents a paradigm shift in how banks understand and serve customers. By combining AI's analytical power with comprehensive behavioral data, financial institutions can create more secure, personalized, and efficient services than ever before.
For consumers, this means better protection against fraud, more relevant financial products, and smoother banking experiences. However, it also requires active engagement in understanding how personal data is used and protected.
For financial institutions, IoB offers competitive advantages through improved risk management, enhanced customer experiences, and operational efficiency. Success depends on implementing robust privacy protections, ensuring algorithmic fairness, and maintaining transparency with customers.
The future of banking lies in responsible IoB implementation – harnessing behavioral insights to create value while respecting privacy and promoting financial inclusion. As we move toward a world where 40% of the global population interacts with IoB systems, the institutions that balance innovation with ethical responsibility will define the next era of financial services.
The transformation is already underway. The question isn't whether IoB will reshape banking, but how quickly and responsibly this evolution will unfold.
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