Latest banking technology trends: a comprehensive overview

The democratization of data, fueled by technology and social media, has empowered customers to make informed decisions and demand more from their financial institutions. Alongside the rapid advancements in technology, customer expectations are evolving at an unprecedented pace. They seek a seamless blend of digital convenience and personalized service from their banks: Generative AI, data analytics, and open banking promise to unlock massive customer benefits. The convergence of these technologies creates a new banking era characterized by greater innovation, efficiency, security, and customer-centricity.

Technologies at the forefront of banking: shaping the way we bank

With the erosion of traditional industry boundaries and customer expectations shaped by experiences across sectors, banks now face growing pressure to offer services that mirror those found elsewhere. Generative AI, machine learning, cloud computing, data analytics, and blockchain are transforming how banks operate, interact with customers, and manage risk. Let’s take a closer look at banking technology trends and their impact on the banking industry.

1. Generative AI in banking: from customer experience to risk management

AI and automation have long been a part of the banking industry. Machine learning/deep learning algorithms and natural language processing (NLP) techniques are widely applied in automated trading, risk management modernization, and investment research. However, recent advancements in generative AI present a transformative opportunity to further streamline and automate tasks, ushering in a new era of banking technology trends.

Despite significant investments in automating various functions across the transaction life cycle, numerous tasks still rely on human expertise. However, the advent of large language models (LLMs) holds immense potential to streamline and automate a wide range of tasks, from marketing material generation to coding. Generative AI is gaining traction in various domains, including fraud detection, anti-money laundering (AML) efforts, client communication and marketing strategies, product fit evaluations, memo drafting, and research-driven report generation.

Moreover, generative AI offers cost savings and enhances employee productivity. LLMs represent a transformative step towards a more efficient and customer-centric banking landscape. While enhancing the customer experience remains a top priority, banks are exploring how emerging technologies can streamline and optimize risk, compliance, and operational tasks.

For instance, generative AI, could revolutionize credit risk assessments by expediting the process, enabling mortgage applicants to receive real-time notifications about missing or incomplete documents, and boosting the overall productivity of customer-facing teams.

Financial institutions are carefully approaching integrating AI into customer applications, mindful of the potential for ethical and security risks. However, as banks gradually master the safe utilization of generative AI, they can bring about a wave of improvements. These improvements include enhanced chatbots, personalized marketing campaigns, and streamlined processes, reducing wait times.

According to Deloitte, banks will need to enhance their anti-fraud capabilities using generative AI technologies and third-party data. It will enable them to train their authentication and fraud detection models to anticipate criminal strategies and block their attempts. They should forge closer partnerships with technology companies to develop multimodal biometric security systems that assess multiple factors simultaneously, such as fingerprints, natural speech patterns, and word choice, to improve fraud detection outcomes and reduce false positives. 

Generative AI's transformative power extends to trading operations, revolutionizing both sell and buy-side perspectives. In the immediate future, traders will leverage LLMs to process vast amounts of text, gaining valuable insights that inform their trading strategies, Deloitte predicts. Leading financial institutions, for instance, have begun employing generative AI to extract trade signals by deciphering speeches and communications from the central banks. These advancements are anticipated to significantly impact the speed and volume of trading activities on exchanges. Additionally, they will launch a new era of demand for innovative market data from exchanges.

The global generative AI in the banking market is projected to reach a value of nearly $9724.5M by 2032, expanding at a compound annual growth rate (CAGR) of 32.7% from $616.3M in 2022. This astounding growth is attributed to several factors, including the abundance of data available to banks, customer information, market analysis, transaction records, and historical trends. These vast datasets can be harnessed to train generative AI models that provide accurate predictions and insights, enhancing decision-making and risk mitigation.

NLP, an AI subfield, holds the dominant position in the technology segment, with a market share of 36%, owing to its transformative impact on the banking industry. NLP-powered tools like chatbots and virtual assistants are revolutionizing customer interactions by providing personalized recommendations, streamlining transactions, and analyzing customer sentiment in real time. This enables banks to enhance customer support and tailor services to meet individual needs. Additionally, NLP integrated with generative AI models is automating document analysis and processing, encompassing tasks such as data extraction, compliance checks, and risk assessments. This streamlines operations, enhances efficiency, and reduces costs.

2. Embedded finance technologies: from products to ecosystems

McKinsey’s Global Banking Revenue Pool predicts that embedded finance products could soon account for half of all banking revenue. Embedded finance, one of the leading banking technology trends, encompasses a wide range of domains, but payments dominate in terms of revenue generation. According to research by EY, the volume of payments processed through embedded channels reached a staggering US$2.5T in 2021 and is projected to surge to US$6.5T by 2025. The ever-increasing pace of market innovation offers established firms a chance to reshape their business models and cultivate new growth opportunities.

Buy now, pay later (BNPL) plans, enabling customers to stagger large purchases into smaller payments and enhance conversion rates for retailers, are rapidly gaining traction. These disruptive models are projected to reach a global transaction volume of $596.7B by 2026, a substantial increase from $141.8B. BNPL offerings, embedded directly into online platforms, are predicted to reach $437B globally by 2027, a 291% increase from $112B in 2022. 

Additionally, the widespread adoption of digital wallets worldwide has revolutionized payments for consumers and merchants. These digital wallets offer many value-added features, including loyalty programs, enhanced security, and open-loop payment systems. As a result, the global number of digital wallet users is anticipated to surpass 5.2B by 2026, up from 3.4B in 2022. 

Embedded finance growth allows banks to expand their customer base, enhance loyalty, and create new revenue streams. To achieve this, banks are moving towards a platform strategy. In the platform scenario, banks own and operate online marketplaces, integrating their financial products with non-financial services to create a cohesive ecosystem for sellers and buyers. To successfully implement platforms, banks will need to employ a range of technologies that address data and analytics, platform infrastructure, and customer experience: 

• Banks will utilize advanced data analytics and machine learning algorithms to glean actionable insights from the gathered data, enabling customer segmentation, risk assessment, fraud detection, and personalized recommendations.
• For their platform infrastructure, banks will develop APIs, adopt microservices infrastructure, and leverage cloud computing platforms.
• To provide a seamless and intuitive customer experience, banks must develop user-friendly interfaces for their embedded finance platforms. An omnichannel approach will ensure that customers can interact with the platform across various touchpoints, including web, mobile, and in-person interactions. 

3. Open banking: open data technologies

Polaris Market Research recently estimated that open banking will be worth $128.12B by 2030. Open banking bridges banks with non-banking financial companies (NBFCs). Third-party developers access data securely through banking APIs, fostering innovations like embedded banking. The rise of banking-as-a-service (BaaS) further expands banks’ outreach and potential revenue streams. Regulators and central banks continue lowering the barriers to data sharing in the United Kingdom, Europe, Australia, Saudi Arabia, Brazil, and Mexico. 

Open banking payments shift power dynamics by leveraging application programming interfaces (APIs). These APIs empower third parties to circumvent traditional intermediaries, paving the way for more innovative financial solutions. This banking technology trend unlocks the potential of Banking-as-a-Service (BaaS), enabling banks to reach new customer segments through third-party partnerships and generate additional revenue streams.

In 2025, open banking will continue to evolve, and several key technologies will be instrumental in its implementation and growth. These technologies enable secure data sharing, improve customer experiences, and foster innovation in financial services:

• APIs: APIs allow financial institutions to securely share customer data and access services provided by third-party providers (TPPs). API standards, such as OAuth 2.0 and OpenID Connect, will continue to be essential for data exchange.
• Blockchain and Distributed Ledger Technology (DLT): Blockchain enhances the security, transparency, and traceability of open banking transactions, particularly in cross-border payments and digital identity verification.
• Artificial Intelligence (AI) and Machine Learning (ML): AI and ML are used to analyze vast amounts of financial data for fraud detection, risk assessment, and personalized customer experiences. Chatbots and virtual assistants enabled by AI will provide efficient customer support.
• Big Data analytics: Analyzing large datasets generated by open banking transactions helps financial institutions gain insights into customer behavior, preferences, and financial trends.
• Cybersecurity solutions: As open banking involves sharing sensitive financial data, robust cybersecurity measures are essential to protect against data breaches and cyber threats.
• Quantum computing: While not mainstream yet, quantum computing has the potential to affect encryption and security in open banking. Financial institutions should stay informed about developments in this area.

These banking technology trends demonstrate the growing impact of open banking. It is rapidly changing how we access and manage our finances, paving the way for new and exciting solutions and broader financial inclusion.

4. AI transforming customer experience: hyper-personalization in banking 

The digital age has brought about a demand for personalized services and products. Banks are now expected to deliver hyper-personalization, which involves using real-time data and behavioral science to provide context-specific and relevant services, products, and pricing. This hyper-personalization is expected to become the new standard of customer service in the banking industry.

A study by Forrester showed that banks with advanced personalization were nearly three times more likely to exceed their goals for cross-selling and acquiring customers than banks with less advanced personalization. Remarkably, they surpassed their self-established digital transformation goals by 171%. Additionally, they demonstrated a heightened ability to comprehend and respond to customer needs.

Efficient hyper-personalization involves leveraging existing data alongside emerging technologies to assist customers in reaching their unique objectives. Technologies that will drive hyper-personalization in banking in 2025 include:

• Artificial Intelligence (AI): AI serves as the cornerstone of hyper-personalized banking. Machine learning algorithms process immense amounts of data to generate tailored recommendations, predict customer behavior, and offer real-time support through chatbots and virtual assistants.
• Big Data Analytics: Banks collect a lot of data, making big data analytics tools indispensable for extracting meaningful insights from this information. These tools enable banks to comprehensively understand their customers' needs, preferences, and financial behaviors.
• Internet of Things (IoT): Data gathered from IoT devices, e.g., smart home sensors and wearable technology, is integrated into customer profiles, providing a holistic view of individuals' lifestyles and preferences. This enriched data empowers banks to deliver hyper-personalized experiences that seamlessly align with customers' daily routines and aspirations.
• Blockchain: Blockchain technology ensures the secure and verifiable nature of transactions, guaranteeing the integrity and transparency of every financial interaction. This inherent security fosters trust and confidence among customers, enabling them to engage in personalized banking activities without concerns about data privacy or transaction integrity.

Among the various applications of AI in banking, improving the consumer experience through greater customer personalization capabilities emerged as the most promising use case, garnering 27.2% of respondents' votes. This highlights the growing recognition of AI's potential to revolutionize how banks interact with their customers, delivering tailored and relevant services that cater to individual needs and preferences.

In addition to personalization, AI is also proving to be a valuable tool for customer fraud detection, with 15.7% of respondents citing it as a key area of AI application. AI algorithms can analyze transaction patterns and identify anomalies in real-time, enabling banks to prevent fraudulent activities before they occur. This not only protects customers' financial assets but also safeguards the bank's reputation and reduces operational costs associated with fraud investigations.

Read more: Digitalization in financial services: top tech and strategies 

5. Technologies for cybersecurity in banking

2025 will witness an accelerated pace of several banking technology trends defining the future of the consumer payments landscape, predicts Deloitte. Consumer spending patterns will continue to transition from cash to digital payment channels. However, this growth in digital and real-time payments, combined with the advancement of artificial intelligence, will also escalate the sophistication and complexity of fraud and cyber threats, making them increasingly difficult to prevent and detect. In response to this surge in digital transactions, banks are proactively investing in cutting-edge security measures that include blockchain technology and biometric authentication to safeguard customer information and financial assets. Critical cybersecurity technologies for banks include:

• Endpoint security: Shielding individual devices like computers and mobile phones from cyber threats.
• Artificial Intelligence (AI) and Machine Learning: Employed for threat detection and anomaly recognition in user behavior.
• Blockchain: Enabling a secure and tamper-proof ledger for transactions and data storage.
• Biometric authentication: Leveraging unique physical or behavioral characteristics for secure user authentication.
• Security information and event management (SIEM): Empower banks with real-time monitoring and response to cybersecurity incidents.

6. Technologies enabling the ESG 

Environmental, social, and governance (ESG) considerations are poised to revolutionize business operations over the next decade. This latest trend in banking technology will compel businesses across industries to undergo substantial transformations, potentially even adopting net-zero emissions strategies in the coming decades. The transition will entail significant investments, presenting banks with immense opportunities. 

With the ESRS regulaitons bacoming active back in January 2024, companies across the European Union are gearing up to comply with the new sustainability reporting standards. These standards will mandate a standardized approach to reporting ESG matters, bringing greater transparency and consistency to corporate sustainability disclosures. This shift is poised to significantly impact businesses, driving them to adopt more sustainable practices and enhance their overall ESG performance.

To efficiently gather ESG data, companies can leverage software tools that seamlessly integrate with their existing systems, such as ERP, CRM, or IoT, enabling automated ESG data extraction. Additionally, cloud-based platforms provide secure and remote storage, access, and sharing capabilities for ESG data.

AI-powered tools can be used for ESG data analysis, identifying hidden patterns, trends, and potential risks, while machine learning tools provide tailored recommendations, and natural language processing tools facilitate clear communication of insights.

Wrap-up

Banking is undergoing a rapid transformation driven by technological innovations, changing customer expectations, and evolving regulatory requirements. Generative AI, in particular, is poised to reshape various aspects of banking, from customer experience to risk management. Additionally, technologies such as cloud computing, data analytics, blockchain, and robust cybersecurity measures will improve banks' operations and customer service, positioning them for long-term success. Successfully implementing and leveraging these technologies requires expertise and resources that may not be readily available within most banks. A reputable software provider can guide banks through the process of selecting, implementing, and integrating digital solutions into their existing systems and help the bank achieve its desired return on investment.