From Welcome to Wow: How to Optimize Customer Onboarding for Banks with Camunda and Machine Learning (ML) Models

The banking industry in the UAE is undergoing a significant transformation, by fast adopting technological advancements, primarily— machine learning. Notably, the UAE ranks sixth globally in digital banking penetration, with recent studies highlighting it as one of the world's foremost adopters of digital banking. This momentum will intensify, with a projected increase of 22 % to reach 41% in digital banking penetration by the end of 2027.  

Why the paradigm shift towards Machine Learning? 

This technological shift is empowering banks to extract valuable insights from extensive data sets, enhancing decision-making processes and offering tailor-made customer experiences.  

A seamless and personalized onboarding journey is crucial to cultivate strong customer relationships, increase customer satisfaction, loyalty, and revenue expansion. Banks leverage the Camunda platform, a powerful workflow automation platform, along with machine learning models to enhance this process. Camunda, a lightweight, Java-based framework, helps streamline complex processes, automates tasks, and amplifies operational efficiency, enriching customer experiences. It empowers banks to automate and coordinate intricate onboarding workflows, facilitating seamless transitions between various departments and stakeholders participating in the process. By integrating machine learning models into the Camunda platform, banks can elevate their decision-making, automate tedious manual tasks, and create personalized customer onboarding experiences. 

In this article, we dive deep into the symbiotic potential of machine learning models and the Camunda workflow automation platform. We also elucidate how these can synergize to streamline customer onboarding processes. 

How to Optimize Onboarding Process with Camunda and Machine Learning 

Let's consider the bank account opening process, which typically involves the following sequential steps: 

 The Camunda workflow automation platform includes two crucial components for modeling and executing steps in a business process: user tasks and service tasks. 

1. User tasks are essential for activities that necessitate human interaction or input. These must be carried out by end-users or participants involved in the workflow. For instance, during the account creation process, a user task to ‘verify identity’ could prompt a bank employee to review and validate the submitted identification documents before proceeding with the account setup. 

1. Service tasks are employed for automated activities performed by external systems, applications, or services. For instance, a ‘credit check’ service task might involve an automated integration with a credit scoring service to assess customer creditworthiness as part of the onboarding process.   

By integrating machine learning models into the decision points of a Camunda workflow, financial organizations can capitalize on data-driven insights to improve decision-making. Machine learning algorithms can analyze historical data, identify patterns, and consider contextual information to offer valuable recommendations or predictions — optimizing decision-making within automated processes. 

Clustering Algorithm: An effective application of the DBSCAN algorithm in a customer onboarding process. 

The clustering algorithm is a powerful unsupervised machine-learning technique that groups similar data points into clusters based on specific criteria or similarity measures. Its main objective is to reveal patterns, structures, or relationships within the data without prior knowledge of class labels. Among the various clustering algorithms, some commonly used are K-MEANS, DBSCAN, Gaussian Mixture Model (GMM), and Mean-Shift. 

Let's explore the effective application of the DBSCAN algorithm in a customer onboarding process.  

DBSCAN or Density-Based Spatial Clustering of Applications with Noise is useful for grouping data points based on their density in the data space. For instance, a simplified dataset with four numerical features: Employer Type, Employment Type, Income Type, and Age. Each data point in this dataset represents an individual customer, and the goal is to identify customer clusters. 

Using the DBSCAN algorithm, we can effectively discover patterns and group customers based on the features. This clustering can provide valuable insights into customer segments, enabling personalized and targeted strategies for the customer onboarding process.

Diving into the science behind DBSCAN algorithm used to achieve clustering for our datasets 

Though you can use the Scikit Library in Python, let's delve into the underlying mathematics behind it. We will look at the mathematical application of the DBSCAN algorithm on the given datasets for clustering. It involves the following steps: 

• Step 1: Define ε (Epsilon) 

We start by selecting a suitable value for ε. For instance, let's assume ε = 10000, which implies that data points within a radius of 10000 units are considered as neighbors. 

• Step 2: Set MinPts (Minimum Points) 

Next, we set MinPts = 3, indicating that a core point must have a minimum of 3 data points (including itself) within its ε-neighborhood. 

• Step 3: Calculate Distance 

With the parameters set, we proceed to calculate the distance between each pair of data points using a distance metric such as the Euclidean distance. 

 Euclidean Distance Formula: d (p, q) = √((x₁ - x₂)² + (y₁ - y₂)² + ... + (z₁ - z₂)²) 

where (x₁, y₁) and (x₂, y₂) are the coordinates of points p and q, respectively. 

Now, let’s calculate distance between Customer ID (1) & Customer ID (2) 

Distance = √ ((26 - 25) ² + (40000 - 45000) ² + (2 - 1) ² + (2 - 1)²) 

  = √ (1 + 2500000000 + 1 + 1) 

               C(1) & C(2) = √2500000003 ≈ 50000 units. 

So, for Customer ID (1) & Customer ID (3) 

Distance = √ ((27 - 25) ² + (55000 - 45000) ² + (1 - 1)² + (1 - 1)²) 

  = √ (4 + 2500000000 + 0 + 0) 

              C(1) & C(3)         = √2500000004 ≈ 50000 units 

Similarly, we need to apply this to all possible combinations and obtain the matrix. 

• Step 4: Identifying Core Points 

Next, we identify core points based on ε and MinPts as follows: 

1. For each data point, we count the number of points within ε distance (ε = 10000) (including itself).
2. If the count is greater than or equal to MinPts (3), we mark the data point as a core point. 

On applying the above principle, we obtain the following core points for our datasets: 

Core Points: Customer 1, Customer 2, Customer 3, Customer 5, Customer 7, Customer 8, and Customer 9. 
 

• Step 5: Density-Reachable Points & Clustering 

For each core point, we find all other data points that are within ε distance. If a data point is within ε distance of another core point, it is considered density-reachable from that core point. 

1. We start from an unvisited core point and collect all directly and indirectly density-reachable points to form a cluster. 
2. We continue expanding the cluster until no more density-reachable points can be added. 
3. We move to the next unvisited core point and repeat the clustering process.
4. We continue until all data points are assigned to clusters or marked as noise. 

On applying the above principle, we obtain the following clusters for our dataset using the given parameters: 

We can observe that Customer ID 6 (32 years old, with an income of 72000 and employed by an employer other than a bank-listed employer) is not a core point. Also, no separate cluster is formed for this customer. 

Implementing Camunda Workflow Automation with Machine Learning: Steps and Advantages 

Camunda's integration with Machine Learning forms a powerful and synergistic combination. These steps help in effectively implementing clustering algorithms on Camunda workflow engines.   

Ten steps to implement clustering algorithms on Camunda workflow engines effectively.  

1. Data preprocessing: Clean, transform, and normalize your data to ensure accuracy and relevance. This step prepares your data for analysis by removing inconsistencies or outliers that could impact the clustering results.  
2. Algorithm selection: Choose appropriate clustering algorithms based on your data and objectives. Popular choices include k-means, hierarchical clustering, and DBSCAN. Selecting the right algorithm is crucial for obtaining actionable insights from your data.  
3. Integration with Camunda: Incorporate clustering into your Camunda workflows seamlessly. Define specific steps within the workflow where clustering analysis should occur—leverage Camunda's integration capabilities to invoke clustering scripts or algorithms and pass relevant data.  

1. Real-time batch processing: Set up real-time or batch data processing through Camunda, depending on your use case. Real-time processing allows for immediate insights, while batch processing can be scheduled to analyze data at specific intervals.  
2. Clustering output as workflow variable: Capture clustering results as workflow variables within Camunda. These variables can inform subsequent steps in the workflow, enabling dynamic decision-making based on clustering outcomes.
3. Personalized onboarding: Utilize clustering insights to tailor onboarding experiences for customers. For example, cluster analysis can help identify distinct customer segments, allowing banks to offer personalized services and recommendations during onboarding.  
4. Continuous learning: Implement a mechanism to update clustering models regularly. As new data becomes available, retrain the clustering model to ensure it adapts to evolving trends and patterns.  
5. Monitoring & evaluation: Establish monitoring mechanisms to track the performance of your clustering algorithms. Monitor cluster stability, accuracy, and any shifts in customer segments over time; make adjustments as needed.
6. Scalability & performance: Ensure your clustering implementation is scalable to accommodate larger datasets and growing customer bases. Optimize algorithms and infrastructure to maintain efficient performance.  
7. Optimize implementation: Regularly assess the effectiveness of your clustering implementation. Refine data preprocessing, algorithm selection, and integration processes to enhance the quality of insights derived from clustering.

Advantages of using clustering algorithms in customer onboarding:  

• Customer segmentation: Clustering groups of customers with similar characteristics, behaviors, or preferences, enabling tailored onboarding strategies for each customer segment. This personalization boosts customer satisfaction and engagement. 
• Personalized onboarding experience: Segmentation facilitates customized onboarding experiences, including targeted processes, welcome messages, and product recommendations. This enhances engagement and retention rates. 
• Fraud detection and risk assessment: Clustering detects anomalies in customer behavior, aiding in early fraud detection and risk mitigation. 
• Improving product offerings: Clustering identifies customer needs and pain points, guiding product enhancements and development to align with customer expectations. 
• Optimizing communication channels: Clustering helps identify preferred communication channels for each segment, leading to higher response rates and engagement during onboarding. 

End Note 

Machine learning models with the Camunda workflow automation platform bring countless possibilities for businesses. For those who are aiming to optimize and enhance their processes, Machine Learning drives intelligent and efficient automation by infusing data-driven insights and predictive capabilities into the workflow's decision-making points. 

Nagarro is known for its workflow automation and machine learning solutions as we empower financial institutions to be adept and utilize their potential within the Camunda platform. After delivering a record number of groundbreaking solutions, we are a valuable partner in the transformative journey. Our solutions extend to workflow customization, predictive model creation, intelligent decision-making, and more.  

In the next article, we will delve into how decision trees take center stage in predicting customer behavior. We will further showcase how these enable financial institutions to thrive in an era of enhanced efficiency, customer-centricity, and forward-thinking strategies.