Exploring the World of Contextual Banking: Driving Business Success by Enhancing Customer Experience

An extensive look into Contextual Banking by Taner Akcok

Contextual banking refers to the personalisation of financial products and services based on the context in which they are being used. This could include offering different products or services based on the user’s location, time of day, or other factors.

Essentially, contextual banking aims to offer the right product to the right customer through the right channel at the right time. We can consider the intersection area of “Bank Offerings” and “Customer Needs” as the contextual banking service zone.

There are three main goals of contextual banking: revenue generation, risk mitigation, and cost efficiency. To achieve these goals, contextual banking involves essential activities such as building new products, generating and enhancing data, and creating an ecosystem around the bank.

In this article, we will delve deep into the various actors involved in contextual banking, as well as the activities and goals mentioned above.

Understanding the Mission and Key Actors in Contextual Banking

Contextual Banking service zone is driven by both product offerings and customer needs. The bank’s internal and external capabilities can be considered part of the product offerings, which are affected by changing customer needs, regulations, competition, technology, and the ecosystem.

The market needs are changing rapidly, creating a need for new financial products and services. Regulations, such as PSD2 and GDPR in Europe, are making the market more connected and protecting customer privacy.

To provide a smooth customer experience, banks must adapt to these changing regulations. In addition, new technologies, such as mobile-first and wearable devices, are changing the customer experience and forcing banks to build more partnerships with the ecosystem.

On the other hand, customer needs and behaviours are changing. Rather than visiting bank websites, customers are seeking personalised services from comparison portals or only considering bank offerings if they are timely. They want to connect their technologies and other services in a secure, seamless way.

To manage these drivers and build a contextual experience, it is important to understand the various actors involved in contextual banking. The first actor is the “Bank“, which has core banking capabilities and embedded finance offerings that it serves through its banking front-end or other channels.

The second actor is “Fintechs“, which help banks enhance their offerings or complete the customer experience. Banks can collaborate with fintech partners in different ways, as discussed in the article on embedded finance.

The third actor is “Digital Companies“, which can be software providers (such as ERP, invoicing, HR systems, etc.) or infrastructure services (cloud, CRM, platform, etc.). Banks can acquire more data from these digital companies to have a holistic view of the customer, which enables them to offer the right financial product at the right time. For example, cash flow prediction for SMEs can help the bank offer the right financial product to the SME at the right time.

The fourth actor is “Ecosystem Players”, which can be from various industries such as real estate, automotive, and retail. Banks can collect a holistic view of customers or merchants to offer the right financial products at the right time.

The last actor is the “Customer“, which can be a private bank, investment bank, or corporate bank customer. Customers always desire personalised products that are more connected, secure, and easy to use. Banks can provide the right product at the right time by leveraging their own core and embedded banking capabilities, fintech partnerships, integrations with digital companies, and collaborations with ecosystem partners.

Exploring the Three Main Goals and Use-Cases

As mentioned in the introduction section, “Contextual Banking” has three main goals; revenue generation, risk mitigation and cost efficiency. There are some common use-cases for each of these goals and business value impact for the use-cases.

As shown into the visual as well, there are some Revenue Growth opportunities with some use-cases such as:

1. More effective customer targeting: One way to improve customer targeting in banking is to gather detailed customer data and use it to segment the market into smaller, more specific groups.By using techniques such as predictive modelling and data analysis, banks can tailor their marketing efforts to reach the most valuable and receptive customers, leading to more effective targeting and higher conversion rates.
2. Spot clients who are likely to switch another bank: To spot clients who are likely to switch to another bank, it is important to regularly review customer satisfaction levels and identify any potential pain points or dissatisfaction with the current bank’s products or services.Additionally, monitoring the customer’s financial behavior, such as increased usage of competitors’ products or a decrease in engagement with the current bank, can also be indicators of a potential switch.
3. Identify commercial opportunities per single client by looking at internal data and external information: Identifying commercial opportunities per single client in banking involves analysing both internal data, such as the customer’s past transactions and product usage, as well as external information, such as market trends and the customer’s industry or profession. By combining this information, banks can tailor their products and services to meet the specific needs and preferences of each individual client, resulting in more personalised and profitable relationships.
4. Leakage identification price optimisation: Leakage identification in banking refers to the process of identifying and correcting areas where a bank is losing revenue or profits. One strategy for identifying leakage is to perform a thorough analysis of pricing structures, looking for opportunities to optimise pricing in order to maximise profits and minimise losses. This can involve analysing customer behaviour and market conditions to determine the optimal price points for different products and services.
5. Micro-Segmentation: Micro-segmentation in banking refers to the process of dividing the market into very specific and detailed groups, based on individual characteristics and behaviours. By using advanced analytics and data-mining techniques, banks can create highly targeted and personalised marketing campaigns, resulting in more effective outreach and higher conversion rates.
6. 1-to-1 pricing: One-to-one pricing in banking refers to the practice of offering customised and personalised pricing to individual customers, based on their specific needs and behaviours. This approach can be used to increase customer satisfaction and loyalty by offering more tailored and relevant products and services, as well as to optimise pricing for maximum profitability.
7. Transactional analytics: Transactional analytics in banking involves analysing data from financial transactions in order to better understand customer behaviour and identify trends and patterns. By using advanced analytics tools and techniques, banks can gain insights into how customers are interacting with their products and services, and use this information to optimise marketing efforts, improve the customer experience, and drive profitability.
8. Churn Reduction Analytics: Churn reduction analytics in banking refers to the use of data and analytics to identify and prevent customers from leaving the bank and switching to a competitor. By analysing customer data and behaviour, banks can identify potential churn risks and take proactive steps to retain these customers, such as by offering personalised promotions or addressing any issues that may be causing dissatisfaction.
9. Customer Experience Analytics: Customer experience analytics in banking involves using data and analytics to understand and improve the customer journey throughout their interactions with the bank. By analysing customer feedback and data on their behaviours and interactions with the bank, banks can identify areas for improvement and optimise the customer experience, leading to increased customer satisfaction and loyalty.
10. Insight based investment advice: Insight-based investment advice in banking involves using data and analytics to provide personalised recommendations to customers on how to invest their money. By analysing a customer’s financial goals, risk tolerance, and investment history, banks can offer tailored advice on the most suitable investment options, helping customers to make informed decisions and reach their financial objectives.
11. Optimise purchase funnel leakage: To optimise the purchase funnel in banking, it is important to identify and address any leakage, or points where potential customers are dropping out of the process before completing a purchase. By analysing customer data and behaviour, banks can identify where in the funnel leakage is occurring and take steps to improve the customer experience and encourage conversion, such as by simplifying the process or addressing any pain points or issues that may be causing customers to drop out.
12. Retention / Churn Reduction analytics: Retention or churn reduction analytics in banking involves using data and analytics to identify and prevent customers from leaving the bank and switching to a competitor. By analysing customer data and behaviour, banks can identify potential churn risks and take proactive steps to retain these customers, such as by offering personalised promotions or addressing any issues that may be causing dissatisfaction.
13. Dynamically recommend next conversation/trade: Dynamically recommending the next conversation or trade in banking involves using data and analytics to provide personalised recommendations to customers on what actions to take next in their financial journey. By analysing a customer’s financial goals, risk tolerance, and investment history, banks can offer tailored advice on the most suitable options for their next steps, helping customers to make informed decisions and reach their financial objectives.
14. Customer behavioural segmentation: Customer behavioural segmentation in banking involves dividing the customer base into groups based on their behaviours and characteristics, such as their spending habits, financial goals, and risk tolerance. By segmenting customers in this way, banks can tailor their products and services to better meet the needs and preferences of each specific group, resulting in more personalised and profitable relationships.
15. De-bias Investment Decisions: De-biasing investment decisions in banking involves using data and analytics to eliminate biases and make more objective, data-driven decisions when it comes to investing. This can involve using advanced analytics tools and techniques to identify and correct for biases, as well as implementing processes and controls to ensure that decisions are based on data rather than subjective opinions or preconceptions.
16. Generate Investment Ideas Using big data: Generating investment ideas using big data in banking involves leveraging large datasets and advanced analytics tools to identify potential investment opportunities and make more informed decisions. By analysing market trends, customer behaviours, and other relevant data, banks can generate new ideas and insights that can help them to identify attractive investment opportunities and make more profitable decisions.

There are several use cases for risk mitigation in banking, including:

1. Digital credit assessment: Digital credit assessment in banking involves using data and analytics to evaluate the creditworthiness of potential borrowers. By analysing a wide range of data sources, such as financial records, credit scores, and other relevant information, banks can make more informed and objective decisions about whether to grant credit and at what terms.
2. Advanced Early warning systems: Advanced early warning systems in banking involve using data and analytics to identify potential risks or issues in a timely manner, allowing the bank to take proactive steps to mitigate those risks.By continuously monitoring a wide range of data sources, such as financial records, market trends, and customer behaviour, banks can detect potential risks or issues before they become significant problems and take action to prevent or minimise the impact.
3. Credit collection analytics: Credit collection analytics in banking involves using data and analytics to optimise the process of collecting debts from delinquent borrowers.By analysing data on customer behaviours, financial histories, and other relevant factors, banks can identify the most effective strategies for recovering debts, such as targeting specific customer segments or using certain communication channels.
4. Fraud detection analytics: Fraud detection analytics in banking involves using data and analytics to identify and prevent fraudulent activity. By continuously monitoring transactions and analysing patterns and behaviours, banks can detect potential fraud and take action to prevent it from occurring, protecting both the bank and its customers.
5. Fraud detection KYC: Fraud detection through KYC (know your customer) processes in banking involves using data and analytics to verify the identity of customers and ensure that they are not attempting to commit fraud. By collecting and verifying data on customers’ identities, banks can better detect and prevent fraudulent activity, protecting both the bank and its customers.
6. Support RMs/Credit Offices via Digital Workflow: Digital workflow support for relationship managers (RMs) and credit offices in banking involves using data and analytics to streamline and optimise the process of managing customer relationships and issuing credit. By leveraging advanced analytics tools and techniques, banks can more efficiently and effectively manage customer interactions, making it easier for RMs and credit offices to handle large volumes of customers and transactions.
7. Decision engine and/or RM recommendation tool: A decision engine and/or RM recommendation tool in banking is a software application that uses data and analytics to provide personalised recommendations to relationship managers (RMs) on how to interact with and serve customers. By analysing customer data and behaviour, these tools can help RMs to make more informed and tailored recommendations, resulting in more effective and efficient customer interactions and higher levels of customer satisfaction.
8. Identify clients who are likely to become riskier/problematic: To identify clients who are likely to become riskier or problematic in banking, it is important to continuously monitor customer data and behaviour and look for any signs of potential issues. This can involve analysing financial data, such as changes in creditworthiness or payment patterns, as well as looking for changes in customer behaviour that may indicate potential risks or problems. By identifying these issues early on, banks can take proactive steps to mitigate risks and prevent problems from occurring.
9. Identify suspicious client transactions/behaviour: To identify suspicious client transactions or behaviour in banking, it is important to continuously monitor customer data and transactions for any patterns or anomalies that may indicate potential fraudulent activity. This can involve using advanced analytics tools and techniques, such as machine learning and data mining, to identify unusual or suspicious patterns in customer data and transactions, and taking appropriate action to prevent or mitigate potential fraud.
10. Improve detection rate (OCR): To improve the detection rate of OCR (optical character recognition) in banking, it is important to use advanced analytics tools and techniques to optimise the process of extracting and interpreting data from scanned documents. This can involve using machine learning algorithms to improve the accuracy of OCR systems, as well as implementing processes and controls to ensure that data is accurately and consistently captured and interpreted. By improving the detection rate of OCR, banks can more efficiently and effectively extract and use data from scanned documents.
11. Identify potential fraud cases through patterns of past fraud: To identify potential fraud cases through patterns of past fraud in banking, it is important to use data and analytics to analyse the characteristics and behaviours of past fraud cases and look for similar patterns in current and potential customers. By identifying common patterns or characteristics among past fraud cases, banks can develop more effective fraud prevention strategies and better detect and prevent potential fraud in the future.

There are several use cases for cost efficiency in banking, including:

1. Geospatial Analytics: Geospatial analytics in banking involves using data and analytics to understand and analyse geographical patterns and relationships, such as the location and distribution of customers, branches, and other relevant data points. By analysing geospatial data, banks can gain insights into customer behaviours and trends, optimise the location of branches and other physical assets, and make more informed decisions about where to allocate resources.
2. Automation and Productivity optimisation: Automation and productivity optimisation in banking involves using data and analytics to streamline and optimise processes and tasks, resulting in increased efficiency and productivity. By identifying bottlenecks and inefficiencies in current processes and using advanced analytics tools and techniques, banks can automate repetitive tasks and optimise workflows, freeing up staff to focus on more valuable and strategic activities.
3. Advanced financial analysis and reporting: Advanced financial analysis and reporting in banking involves using data and analytics to generate in-depth insights and reports on the financial performance and health of the bank. By leveraging advanced analytics tools and techniques, banks can analyse a wide range of data sources, such as financial statements, market trends, and customer behaviour, to identify trends and patterns and make more informed business decisions.
4. Maximise frontline productivity defining the optimal service model: To maximise frontline productivity in banking, it is important to define the optimal service model by analysing data and customer behaviours to determine the most effective and efficient ways to serve customers. By using advanced analytics tools and techniques, banks can identify the most effective channels, processes, and resources for serving customers, leading to increased productivity and customer satisfaction.
5. Predictive HR: Predictive HR in banking involves using data and analytics to predict and prevent potential HR-related issues, such as employee turnover or skill gaps. By analysing data on employee performance, satisfaction, and other relevant factors, banks can identify potential risks and take proactive steps to address them, leading to a more effective and efficient HR function.

The Importance of Leveraging Data in Contextual Banking

Having a strong data strategy is crucial for executing contextual banking, as it helps to ensure that the bank has access to the right data at the right time in order to deliver personalised and relevant banking services to its customers. So that, contextual banking requires a strong data strategy to execute properly.

Most of the banks start with big data lake initiatives which takes long time to see the results. Extensive data lake initiatives are very expensive and the results are long term. This is why, contextual banking data strategy must start with data puddles around business use cases.

These data puddles must have a data vision that aligns with the master data vision of the bank. A data vision is a clear, long-term goal or direction for how the bank intends to use and leverage data to achieve its business objectives. The data vision should be specific and actionable, outlining the steps that the bank will take to achieve its data-related goals. It should also be aligned with the bank’s overall business strategy and be able to adapt to changing business needs and circumstances.

In the context of contextual banking, it is important to create data-driven business requirements that align with the master data vision or the data vision of a specific data puddle. These requirements should be based on the business perspective and address specific business problems or opportunities that the organisation is facing. These requirements could address a better understanding of customers, the identification of trends and patterns in business performance, the automation of business processes, or the support of risk management and other business values.

Based on the bank’s data vision and data-driven business requirements, the data team should start building a future-proof data design. This data design should focus on data acquisition, data enhancement, and data activation strategies. Key activities in this stage include identifying the bank’s data assets, conducting a quality assessment of the data, and aligning the data with the bank’s business requirements. The data design process is an important step in ensuring that the bank has the right data in place to meet its business goals and objectives. By building a data design that focuses on data acquisition, enhancement, and activation, the bank can ensure that it has access to the right data at the right time and that the data is of sufficient quality and relevance to support decision-making and drive business value.

Based on the outputs of the data vision, business requirements, and data design, the bank can start working on the data architecture. Data architecture covers the overall design and structure of the bank’s data assets, including the data models, data storage systems, and data flow processes used to manage and access data. From the perspective of contextual banking, this data architecture should prioritise principles such as data integration, data storage, data access, and data quality, in order to ensure that the bank has the right data in place to support its operations and decision-making, and to effectively leverage the power of data to drive business value.

Of course, this data design and architecture must be carefully operated and maintained. Data operations and management should focus on tasks such as data collection, data processing, data quality management, data security and governance, and data analytics and reporting integrity. The data and business teams should define quality metrics for these indicators and carefully monitor and manage them to support contextual banking use cases.

The next important step, after ensuring that the data and data operations are ready, is to generate insights from the data. Many banks make the mistake of thinking that building dashboards is the same as generating insights. However, in order to truly serve insights, there are a few steps that must be taken in advance.

1. First, it is important to consider the usability of the data. Data is often consumed by business and marketing teams, rather than data scientists, so it is helpful to abstract a human-readable layer of dimensions and metrics on top of the data to make it more accessible across the bank.
2. Second, the organisation needs the ability to drill down into the data for more detailed analysis. Rather than relying on static dashboards, the organisation needs the ability to add additional dimensions (such as product group, product, or channel) to better understand changes in sales figures or other metrics.
3. Third, the organisation needs strong data management capabilities, including the ability to save filters or segments of data for future analysis. These segments can be rule-based or semantic, but it is important to make them dynamic so that they can continuously grow with new users and shrink with existing users who no longer meet the criteria. For example, a segment identifying car owners based on “>3 gas station spendings in the last 2 months” should constantly grow with new users and existing users should be removed from the segment if they no longer meet the criteria.
4. Forth, activating the data and streaming segments to action-related services is essential. Simply reaching the right data or generating insights from the data is not enough if they are not actionable. For instance, if you segment customers who are decreasing their debts and have trustworthy cash flows, you can stream this segment into a recommendation tool or emailing service to offer further investments.

Segments and segment streaming are the essential needs of the contextual banking use-cases. Here are some examples for customer segments and actions that you can take according to the business goals:

While generating the insights, it is essential to create dashboards for the health check of the decision-making metrics. While building the dashboards, be sure that actionable analysis are pinned into dashboards, they can be filterable by time, segment and some analysis variants and linked to the analysis that users can drill down into the data for have further discoveries.

Designing a Target Business Architecture for Contextual Banking

Target business architecture design is crucial for the success of contextual banking, as they determine how the bank is structured and how it works with external partners to deliver personalised and relevant products and services to customers. A well-designed business architecture can help to ensure that the bank is agile and responsive to the needs of its customers, enabling it to deliver the most appropriate products and services for each individual customer.

Similarly, partnerships with external organisations can help to broaden the bank’s reach and enhance its ability to deliver relevant and valuable products and services to its customers. By building strong partnerships with complementary organisations, banks can access a wider range of resources and expertise, enabling them to better serve their customers and stay ahead of the competition. Therefore, it is important for banks to carefully consider their business architecture design and ensure that they are aligned with their business goals and customer needs. This may involve reorganising the teams and partnerships into smaller, more agile units or implementing processes and technologies that enable the bank to more easily adapt to changing customer needs and work effectively with external partners.

Target business architectures typically have two main layers: the ingestion layer and the serving layer. The ingestion layer represents in-house or third-party functionality or data ingestion capabilities. A contextual banking platform should collect various types of data from different sources to enhance the user data and enable a holistic view of the customer. The serving layer represents the serving of contextual banking capabilities in the bank’s digital assets or third-party platforms. The collected data and functionality from the ingestion layer can trigger contextual activities (as described in “The Importance of Leveraging Data in Contextual Banking” section) into online banking, channels, or third-party services.

Here is an example target business architecture for contextual banking. As you can see from the figure:

The platform can enhance its product and service offerings through partnerships with “Functionality Integration Partners”, which can provide capabilities such as account opening, KYC, financial services, and contract detection. Additionally, by partnering with “Data Ingestion Partners”, the platform can collect a holistic view of the client, including contracts, transactions from payment systems, cash flow, account receivables/payables, tenders, and procurements.

The platform can also provide personalised products and offerings to the right customer at the right time through the right channel, using first and third-party channels or partner platforms, known as “Ecosystem Partners”. Banks can integrate their offerings into the platform through “Platform Partners”, allowing them to provide instant financial offers during procurement or supply chain management, among other things.

As a summary, contextual banking requires business goals and opportunity discovery, data foundation readiness, organisational and technological readiness, ecosystem partnerships to ingest or provide functionalities and data, and platform readiness – which we will deep dive into further in future articles-.

The opinions expressed in this article are my own and do not necessarily reflect the views of any company, organisation or group. For further questions or recommendations, please reach out to me via LinkedIN or visit my website.

Taner Akcok