Introduction
In today’s business world, the use of artificial intelligence and machine learning in business processes has become a competitive advantage for organizations. One of the safest and controlled ways to incorporate machine learning into the business environment is through a Business Process Management (BPM) tool. If you want to learn more about why combining Machine Learning with BPM is beneficial, you can read the article “The incorporation of machine learning into business processes with BPM.”
In this article, we explore how machine learning techniques can enhance organizational processes in four specific scenarios:
- Customer churn prevention
- Candidate screening in the hiring process
- Fraud detection in banking transactions
- Optimization of contact requests
Customer Churn Prevention
Maintaining a good relationship with customers is essential for any organization. The impact of losing a customer can be significant, both in terms of economics and reputation. To avoid this loss, many companies are turning to machine learning models that assess the risk of customer churn, also known as churn rate.
For example, a mobile telecommunications company aiming to identify the risk of customer churn can use a machine learning model for prediction. In this case, the model analyzes historical customer data such as usage patterns, call duration, billing information, complaints filed, payment delays, among others, to identify patterns. If the model indicates a high-risk level, preventive measures can be taken to avoid customer attrition.
Below is a diagram depicting a potential process for this scenario:
In this process, the machine learning model evaluates the updated customer information and generates predictions of churn risk. If the model identifies a customer with a high churn risk, an investigation phase is initiated. The company’s sales team can proactively reach out to the customer, offering special discounts, personalized plans, or other incentives to encourage their retention. Finally, an assessment is conducted on the outcomes of the measures taken.
Reviewing Candidates in the Hiring Process
In the process of personnel selection and recruitment, it is common to receive a large number of resumes, making it challenging to thoroughly analyze each one of them. This can result in the loss of highly qualified candidates simply because they were not adequately reviewed. Incorporating Machine Learning into the recruitment process can provide benefits such as speeding up candidate review and facilitating the identification of top talent.
One way to achieve a more efficient resume review is by using OCR techniques to extract text from resumes. Additionally, natural language processing techniques can be employed to summarize the extracted text content. This way, the review process can be streamlined by highlighting the most relevant qualifications of each candidate.
Here is an example of the process:
Fraud detection in banking transactions
Another example showcasing the combination of Machine Learning with BPM is the detection of fraudulent transactions in the banking domain. In this case, it is possible to train Machine Learning models to identify suspicious patterns and behaviors in banking transactions.
It is important to note that when selecting the appropriate algorithm for this scenario, effectiveness is not the sole criterion to consider. The algorithm also needs to be explainable, as the bank must have solid arguments to support its decision in case of blocking a suspicious transaction.
Furthermore, it is possible to define that suspicious transactions be sent for manual review. This way, the transaction can be thoroughly investigated, and an informed decision can be made regarding its legitimacy. By conducting manual reviews only when fraud suspicions arise, a smooth user experience is maintained, avoiding unnecessary reviews that could cause inconvenience or delays.
Here is a potential implementation of the process:
Optimization of contact requests
In the commercial website of a product, the contact request form plays a crucial role in interacting with interested visitors. However, it can be challenging for the team responsible for handling these requests to deal with the large volume of messages received on a daily basis.
By using machine learning in business processes, it is possible to develop a model that analyzes the messages sent through the contact request form. The model can examine the message content, detect patterns, and evaluate the relevance of each request. This enables the automatic filtering of irrelevant messages, allowing the team to focus on addressing those requests that require attention.
Furthermore, the process can be further accelerated by using machine learning to automatically classify contact requests based on their type. This classification can be used to direct different flows within the process based on the categorized type. For example, if the message relates to a technical inquiry, it can be automatically redirected to the support team. Similarly, if it is a product demo request, it can be automatically sent to the sales team responsible for coordinating and providing the respective demonstrations. This improves the customer service process and enhances the quality of responses, providing a satisfying experience for both the organization and the customers.
Here is an example of the mentioned process:
Other use cases
With recent advancements in natural language technology, such as the emergence of Bard and ChatGPT with GPT-4, new opportunities have arisen to optimize processes within organizations. These models can be integrated via APIs into various processes to address challenges such as:
- Finding errors in document writing
- Summarizing documents
- Identifying errors in a process
- Generating personalized email writing
- And more!
It is important to note that when using external services, it is necessary to review their compliance with the organization’s privacy policies and ensure the protection of confidential data.
Conclusion
In summary, the combination of machine learning with BPM provides numerous benefits across different organizational processes. From optimizing personnel selection to fraud detection, these techniques enhance efficiency, decision-making, and the overall experience for both organizations and customers. By properly implementing these solutions, companies can gain a competitive advantage and achieve sustainable growth in the current market.
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