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Surprisingly, banks and financial institutions were the slowest to adopt modern digital technologies, such as AI and machine learning — a fact acknowledged by the International Monetary Fund. The ones who did became the leaders in their domain, increasing revenues and offering clients custom-made products and financial services. Today, in the age of big data and advanced analytics, personalization in banking (also called hyper personalization) redefines the bank-customer relationships.
Personalization in banking involves tailoring banking services and products to each customer's unique needs and preferences. It goes deeper than addressing customers by name; it's about understanding their financial goals, spending habits, and risk tolerance to provide them with relevant and timely offerings. By leveraging customer data and advanced analytics, banks can provide personalized services that enhance customer experience, drive loyalty, and boost revenue.
The research shows that customers are ready to commit to the banks that offer relevant and seamless personalized offers while expecting their data to be correctly processed and stored. 75% of surveyed customers are willing to share their data to receive more customized offers.
Personalized banking experience is beneficial for both banking institutions and customers. It helps to build win-win relations between parties through targeted financial services and advice on the one side and loyalty and trust on the other. It is a robust tool that can help businesses achieve significant revenue growth. The research indicates that personalization can typically drive a 10-15% revenue lift, with some companies experiencing even higher lifts, ranging from 5% to 25%. The revenue rise achieved depends on the industry and a company's ability to effectively execute its personalization strategy. For banks, the annual revenue growth can reach 10% on average.
The increasing role of custom banking solutions shows that banks seek to improve customer acquisition, customer service, and onboarding by creating a unique customer experience. In a recent survey, 68% of bank respondents said personalization is essential for their organizations' customer acquisition strategies.
Using personalization, banks can attract new customers even knowing the bare minimum about them. Let's assume a website visitor. How does it work?
First, the company analyzes the visitor’s available data. Initially, it can be mere website activity, user's location, or search history. Second, based on the information obtained, banking institutions provide the most suitable offers for different categories of potential customers to show that they recognize their particular needs. Third, onboarding. Once a person becomes a customer, the bank applies a tailored strategy to introduce their products to the customer and offers personal advice and service, building meaningful and solid relationships.
Why else should banks adapt to the modern economy by introducing hyper-personalization? Not only to gain new customers but also to keep the current clients. Today's customers' expectations from banks are pretty high. Poor customer experience can quickly motivate people to switch banks, leading to 20% client loss. 72% of clients expect the companies to recognize them as individuals and understand their preferences. They value personalization, viewing it as a positive experience that makes them feel special. Consumers respond favorably when brands demonstrate their commitment to building relationships beyond just transactions.
Moreover, by delivering quality customer experience and service, banks can increase revenue, engagement, and conversion rates and improve customer loyalty and retention. Financial companies that enhance customer experience can reap substantial rewards, potentially increasing their deposit share by 16.5%.
Personalized banking experience helps win client's trust. Personalized communications and experiences drive brand consideration and cultivate customer satisfaction and loyalty. When consumers feel understood and valued, they are more likely to engage positively with a company, leading to stronger customer relationships that translate into long-term business growth.
Banks must use digital personalization technologies to deliver quality customer experience and stay ahead of the competition. Let's look closer at them.
AI is used to analyze customers' data and reveal their preferences and financial needs. AI-based analytics suggests tailored solutions for specialized audiences, ensuring customers receive appealing offers and services that address their current financial concerns and narrow product arrays to only relevant ones. It can also predict customer's future needs based on their current behavior.
Google Cloud's banking survey revealed a widespread interest in generative AI (gen AI) technologies among banking executives and consumers in the United States. 92% of executives believe there is high demand for gen AI within the banking industry, and 95% think it would transform the industry. Additionally, 96% of banking respondents reported that increased interest in gen A drives senior leadership to become more involved in technology and IT decisions. This data is supported by the fact that in 2022, the US AI market totaled $103.7 bn, while North America gained 36.84% of the world's market share.
According to Statista, data accuracy emerged as the top priority for global business leaders when assessing the effectiveness of AI-driven personalization strategies, with 47% citing it as the most crucial factor. Real-time data speed and customer retention or repeat purchases followed closely behind, each mentioned by 44% of respondents.
These findings highlight the importance of data-driven approaches to AI-powered personalization. Businesses that prioritize data accuracy and leverage real-time insights can effectively tailor customer experiences, fostering more robust customer relationships and driving personalized banking experiences. By streamlining processes and reducing manual tasks, AI-driven personalization also contributes to operational efficiency and time savings, further enhancing its value proposition.
Big data helps banks and fintechs to get a better understanding of customers and their needs. To present a personalized customer experience, the bank applies client segmentation and profiling based on previously gathered personal information.
Big data analytics is a growing market, expected to reach $655 bn by 2029. It empowers financial institutions to delve into vast troves of information, encompassing internal data sources, such as customer transactions and market data, and external data sources, such as social media sentiment and economic indicators. This comprehensive view allows them to uncover hidden patterns, identify emerging trends, and better understand the complex factors that influence risk, therefore introducing better banking solutions.
How are big data solutions introduced in practice? For example, Bank Santander developed a groundbreaking cloud-native core banking platform, Gravity, which has garnered recognition for enhancing customer interactions and streamlining banking processes. Bank Citi uses a customer analytic record that collects customer data. Then, it links with an automated analytics tool, allowing them to analyze it and recommend relevant services in real time.
The introduction of big data enables businesses to see the whole picture of internal processes and their efficiencies, create multifaceted customer profiles, conduct predictive analysis and make informed decisions.
Related: find out how we developed a custom financial reporting solution to analyze financial data.
Automated services, such as robotic process automation (RPA) algorithms, streamline operations and enhance accuracy. They also can cut costs by automating tedious, repetitive tasks up to 30%. It frees employees to focus on more intricate processes that demand human expertise. Moreover, intelligent automation in the banking sector saves time, reduces costs, increases efficiency and customer satisfaction, and reduces reliance on traditional banking channels.
RPA introduction can minimize routine tasks and works well with data entry, new account creation, client onboarding, data processing, and mapping. Automated client onboarding can reduce time to revenue by 20%.
While RPA is more applicable for standard repetitive tasks, mirroring an individual's actions, intelligent automation (IA) can take more complex workflows. IA encompasses RPA, AI, and cognitive automation to process payments, detect fraud, manage compliance, and generate reports.
Intelligent automation empowers customers to complete tasks such as Know Your Customer verification, document validation, compliance checks, and loan document approval from the comfort of their homes anytime without interacting with a bank agent.
The world's leading banks understand the advantages of automation and actively introduce new technologies in data management. In 2022, JPMorgan Chase Bank won the Excellence in Intelligent Automation award by the Markets Choice Awards. The company uses automation in Business Process Engineering (Process and Task Mining, Process and Decision Modeling), Intelligent Automation (Robotics, Data Transformation, Data Visualization), and Workflow.
The prospects of automated services are up-and-coming. The world banking automation market is expected to grow from $4.41 bn in 2023 to $13.39 bn by 2030. Statista estimates that by 2027, the banking sector will take 33.2% of the global RPA market share.
Virtual assistants (chatbots) offer 24/7 customer support, their most significant advantage compared to human staff. Chatbots constantly learn from customers' usage patterns and can better understand their needs, delivering real-time straightforward answers. Virtual assistants provide customized approaches by offering personalized customer support and recommending fitting products and services.
The research shows that chatbots have become a standard tool for banking interactions, with over 98 million users, or about 37% of the U.S. population, engaging with bank chatbots in 2022. Chatbots are a prevalent feature of the financial landscape, with banks, mortgage servicers, debt collectors, and other financial companies incorporating them into their websites, mobile apps, and social media platforms. Nevertheless, they could be more flawless, can still provide insufficient information, and can be inflexible when dealing with complex issues.
Chatbot technology has to develop further, as inadequate chatbot replies lead to unsuccessful customer experience. The recent findings show that as of June 2022, only a quarter of respondents from the US were happy with chatbot interaction, while the level of dissatisfied customers reached 43%. Nevertheless, 32% called their experience “Neutral.”
The banking, financial services, and insurance sector's chatbot market is poised for exponential growth, with an estimated market value of 6.83 billion U.S. dollars by 2030, compared to 586 million U.S. dollars in 2019, showing a constantly increasing trend.
When discussing personalized cybersecurity, two leading technologies can prove your banking app account is secure from the customer's viewpoint. They are 2-factor authentication and using biometrics.
According to Forrester, 45% of customers are ready to share their data in exchange for personalized product offers, stressing the importance of control and transparency over how their data is used and under what conditions. Personalized cybersecurity shows your customers that your institution is concerned about data privacy.
From a bank's perspective, machine learning is one of the solutions.
Machine learning (ML) algorithms act as vigilant sentinels, continuously scanning transactional data in real-time to detect suspicious activities that deviate from established norms. This proactive approach empowers banks to preempt potential threats before they materialize, safeguarding their financial well-being and reducing the risk of losses.
In 2022, 70% of financial institutions reported more than $500,000 in losses due to fraud. Most (95%) survey participants indicated that their organizations could handle fraud issues internally. In line with this, the most prevalent option respondents chose for implementing in-house cybersecurity measures was the adoption of automation (46%). Other priorities for organizations to tackle fraud internally included the lack of dedicated fraud teams (41%) and the inability to adapt to emerging threat patterns (39%).
Armed with historical transaction data and customer profiles, ML models can identify patterns that may signal fraudulent activity. For instance, sudden spikes in transaction volume, frequent transfers to unfamiliar accounts, or transactions from geographically distant locations can raise red flags. By recognizing these anomalies, ML algorithms can alert banks to potential fraud attempts, prompting swift action to protect customer funds and safeguard the financial system's integrity.
The research shows the positive impact of cybersecurity investments. The companies gained 40% or more return on security investment and at least 18% on data breach cost reductions. These remarkable outcomes have unlocked funding that can be reinvested in bolstering their cybersecurity capabilities.
How to implement banking personalization? A financial institution can develop close connections with its customers in several ways.
Personalized pricing means that banks offer a product with the optimal and appealing price. Today, financial institutions drift away from fixed pricing in favor of customized solutions, as this strategy helps establish long-term trustful customer relations. When it comes to money, every client strives to gain benefits and find the best possible deal from this relationship. Personalized pricing is ready to meet their expectations and can significantly boost company's profits. The study shows that customized pricing can increase expected profits by an additional 19% compared to traditional pricing strategies and by a staggering 86% compared to non-optimized pricing.
Advanced analytics and AI-based solutions help banks develop tailored real-time offers. Here are some examples of applied use cases of the practice:
Banks can analyze customer interaction data and behavior to determine the best action and offer personalized services. For example, knowing that a customer's spending includes a lot of traveling, the bank can offer them an airline credit card, which allows them to collect airline miles and get a flight with a discount. Another example is analyzing new user's activity on the website or app to deliver personalized calls for action. Banks can also use customers' location data to offer relevant services immediately.
Personalized financial advice is implemented through applications with personal financial advisers. The analytics system can evaluate customers' spending habits, advise on wise budget management, or notify clients about duplicating services or better options. It can help achieve the client's financial goals in saving, budgeting, and even investing, ensuring that customers can make more informed financial decisions.
Nevertheless, today, AI financial advice has limited trust among customers, a CFP Board survey finds. Only 37% of responders were ready to take AI-generated financial advice without people's verification. Once checked by the expert, 52% of interviewees were prepared to trust generative AI. The banks should raise trust in AI advice and improve the quality and logic of AI decision-making.
Here are six steps that will assist in the implementation of personalized banking.
At this step, you should define what custom banking solutions you want to introduce, depending on goals, resources, scale of implementation, and expected results. You should clearly understand what you want to achieve and by what means, determine deadlines, and establish progress evaluation criteria. Reshaping internal teams may also come in handy. Personalization programs need cross-disciplinary project teams and a commitment to agile management to succeed. These teams should be organized around specific customer segments or journeys and be skilled in creative, collaborative problem-solving.
If you need help figuring out where to start, you can always ask our FinTech experts for help. Our business analysts and technical specialists are ready to build the roadmap that suits your needs.
When it comes to data management, where do you start? Personalization starts with knowing your customer.
First, figure out what information you already have. What is missing? Examine existing data, investigate data sources and formats, and evaluate the relevance of your current data systems. It will help to get a comprehensive picture of the information available and plan the next steps.
Second, identify the specific data types you need to collect. Remember that you may also find excessive data that needs to be cleaned.
Third, choose the right tools for data unification and management: data management platform, customer data platform, enterprise resource platform, or customer relationship management platform.
A unified banking personalization platform provides quality data collection, processing, and storage, and enables timely updates and data enrichment. Moreover, it is vital to ensure data quality, integrity, and security. You do not need to implement all the solutions simultaneously; start with a small step towards improving client databases.
When you have all this data, you need advanced analytic tools to process it. Introducing AI and machine learning might be helpful, as it saves time and speeds up data processing. These technologies form underlying structures, including data pooling and analysis, behavior patterns and customer propensity identification algorithms, and analytical capabilities to feed information into simple dashboards.
At its best, each customer should receive personalized products, services, or advice based on their needs and banking experience. The personalized banking experience is usually addressed to the client group. Customer categorization is a good practice to start with. It helps to identify and target interests and deliver meaningful experiences, providing customers with timely and relevant insights and suggestions.
As you have different categories of customers, you should develop relevant financial products and services for each group or offer the ones you currently have to the most pertinent segments of customers while delivering them with personalized messaging. You can enhance customized experience across all interactions and products, including home equity, personal loans, credit cards, deposit accounts, and vehicle loans.
Identify the main ways to reach out to the customer. You can use all or just some communication channels. Weekly newsletters and notifications based on customer's location, social media, blogs, and text messages — choose what suits you best. Do not forget that custom banking solutions require tailored content for each client group.
Never underrate real-life customer experience in bank branches. The research conducted by Accenture shows that customers still value offline communication and choose visiting branches for complex and high-value activities. When asked if they liked seeing branches in their neighborhood because it confirmed the providers' stability and availability, more than 60% of responders answered in the affirmative. Below, you can find the results based on age group division:
These are the six steps to navigate you through a progressive and deep banking personalization process.
In a world that challenges classic banking approaches toward clients, where competition has shifted, and new, non-banking institutions have appeared on the stage, banks must keep apace with emerging technologies, implementing the best suitable practices to meet customers' needs. While many banking institutions are still lagging, those who succeed in personalized banking are reaping the benefits.
Let's examine various successful solutions of personalization for banks:
As you can see, intelligent banking personalization can include customer segmentation, various communication channels, well-prepared content tailored for a particular audience, and IT solutions.
Personalization for banks still has many challenges to overcome. The significant areas for improvement are data management and data security.
As with many other advanced analytics-based initiatives, personalized banking efforts will only fall flat without a solid data governance framework. Unfortunately, many banks still rely on inflexible computing systems that need more data infrastructure to support personalization.
The roots of ineffective data management lay in legacy systems and organizational silos.
A legacy system is outdated computing software or hardware still in use. Its main disadvantages are maintenance costs, lack of efficiency, and security breaches. As different legacy systems have relatively weak abilities to integrate, they create data silos. Data silos are responsible for data fragmentation and isolation within the particular system.
Information hoarding, or data siloing, occurs when a single department within an organization maintains exclusive access to a collection of raw data, preventing other departments from accessing and utilizing it. The consequences of data silos extend beyond inefficiency and reach into the realm of trust erosion. When departments hoard information, they create an atmosphere of suspicion and distrust, hindering collaboration and undermining the organization's overall success.
Organizational silos are also leveraged into ineffective data governance but from a different perspective. Their origins lay in a particular company's structure. Each department may have its isolated database and collect, store, and process different types of information separately. It creates obstacles to data unification while businesses strive for more information interconnectivity.
A customer data platform is the solution for data consolidation problems. Built on legacy systems, it can help businesses overcome weak data governance. Banks can collect data from the core system and third-party providers and analyze it on a separate, often cloud-based banking personalization platform. A robust data infrastructure should be the top priority for banks implementing enterprise-wide personalization strategies.
The rise of banking personalization initiatives presents a unique challenge for banks: balancing the benefits of personalization with the need to protect customer data privacy.
Transparency and consent are the cornerstones of responsible data management in personalized banking. Banks must communicate their data collection practices to customers, providing detailed information about the purpose, the types of data collected, and how the data is used. This information should be easily accessible and understandable, allowing customers to make informed decisions about their data privacy. Customers should also be able to withdraw their consent easily at any time.
Banks may use multifactor authentication, encryption, tracking customers' unusual activity, privacy policies, and personnel training to safeguard users' data. They can also advise customers on safe online behavior, such as using strong passwords, avoiding fraudulent websites, monitoring personal accounts, etc. These measures ensure data privacy, the security of financial transactions, and safe payment processing.
AI in personalized banking introduces additional considerations around fairness and explainability. Banks should adopt a human-in-the-loop approach, having a human review and approve any AI-generated recommendations before they are presented to customers. This human oversight ensures that decisions are aligned with bank policies, customers' best interests, and regulatory requirements.
Nevertheless, AI guarantees bank law compliance by identifying risk areas and preventing data theft and money laundering. Depending on the country, the banks must comply with industry regulations and other relevant legislation, such as Payment Card Industry Data Security Standards, California Consumer Privacy Act, General Data Protection Regulation, Bank Secrecy Act, Electronic Fund Transfer Act, etc.
When implemented responsibly, personalization for banks can significantly enhance customer experiences, foster stronger customer relationships, and drive business growth.
Hyper personalization in banking helps attract and retain customers, offering them tailored financial products and services and establishing rich and meaningful communication. Personalized banking proves to be the most efficient approach in the modern financial world and will continue to grow.
If you have a business project and want to learn more about fintech software solutions, feel free to contact us. NEKLO provides web and mobile software development expertise and IT consulting and is ready to help implement your business idea.