Machine Learning in BPA can play a huge role in maximizing the potential of business automation deployments, introducing new capabilities and efficiencies beyond traditional automation.

 

Organizations are increasingly adopting Business Process Automation (BPA) to digitize repetitive, manual business processes and ensure that their employees’ efforts and time are spent on tasks that add real value. AI plays an integral role in automating workflows, but it is only as effective as the AI system’s ability to replicate human behavior, i.e. to think, read, and act as humans do. This is where machine learning (ML) comes into play.

 

The role of AI and Machine Learning in BPA

 

AI has been a key driver of business process automation technologies for quite some time now. But in the recent past, it is the introduction of machine learning to BPA deployments that has led enterprises to stand up and take notice. A number of studies indicate that most business users of ML-powered BPA systems have seen substantial improvement in efficiency.

In business process automation, the most common application of AI is reading and processing data — similar to what humans can do. This data is typically found in business documents, emails, images, voice, and text and has traditionally been the job of human employees to scan manually and process the information. However, AI can undertake smarter, more complex levels of document processing without human intervention.

AI along with ML also enables humans to analyze data and provide valuable insights. Real-time analytics enabled by AI can provide predictive and prescriptive data points to identify existing or potential failures in processes and rectify them in time.

 

What ML can bring to your BPA initiatives: Benefits and Use cases

 

AI integrated with BPA can be programmed to perform a wide range of repetitive tasks so employees do not have to do them. This means it enables automated decision-making capabilities within the systems that perform recurring tasks on their own. But when machine learning is combined with AI, it can power the automated system’s ability to detect and analyze data patterns, “learn” from them, and the consequently, improve business outcomes.

To better understand how machine learning and BPA work together, let us look at some of their key use cases:

 

  • Smarter Process Automation: 

We know that BPA systems and RPA bots can very efficiently perform repetitive, manual administrative and financial tasks. What ML and AI do is give these bots the ability to more closely replicate human behaviors of feeding and accessing information from different IT systems.

Some of the tasks that ML and AI can augment are:

Customer Service Management: Accurately transfer data from email and call center systems into the correct recording systems. For example, updating customer information such as new services added or change in address.

Banking: Recording requests for lost ATM or credit cards and facilitating replacements, access multiple systems simultaneously to update records and manage customer requests, communications, etc.

Financial management: Performing reconciliations of charges and expenses from multiple document types and easily identify and record any mismatches in reporting

Legal services: ML algorithms can be trained to “read” legal documents to extract provisions using natural language processing.

 

  • Cognitive Insight Generation and Engagement

ML algorithms integrated with BPA platforms can detect patterns from within a huge volume of data, interpret what they mean, and make that information available to decision-makers. What adds to its value is that the cognitive insights from machine learning are different from those generated by traditional analytics. Here’s how:

  • ML-generated insights are more detailed and data-intensive,
  • The machine learning models are typically trained on some portion of the data set
  • The models are capable of “learning” and improving – for example, searching for and using new data to predict outcomes, or classifying data and documents more accurately.

Such machine learning-enabled BPA systems, then, be built to perform the following functions:

  • Sales and Marketing: Analyze customers’ purchase behavior across groups to understand what campaigns or offers will work, or even what an individual customer is likely to buy.
  • Banking and Insurance: Real-time credit fraud detection and detecting and flagging fraudulent insurance claims; helping insurers make the right decisions with more accurate and detailed actuarial modeling. Some of the other important use cases of ML in banking include portfolio management, algorithmic trading, and loan underwriting, among others.
  • Quality Control: Detect safety or quality problems in manufactured products such as automobiles, electronics, etc.
  • Digital Advertising: Automate ad targeting or programmatic ad buying to show relevant ads at the right time, to the right audiences.
  • Customer Service: Conversational AI, or chatbots, integrated with machine learning capabilities can be used to engage and interact with hundreds of web visitors simultaneously. Chatbots can also be leveraged as a conversion optimization tool on websites that engage customers with relevant questions related to their purchase. This can simplify the customer’s buying process and help drive conversions.
  • Supply Chain Management: Manual supply chain risk assessments can be replaced by using a machine learning-powered system instead. Once the relevant data points are fed into the system, it presents multiple “what-if” scenarios to analyze and interpret along with potential outcomes, thus aiding decision-makers.
  • Logistics: AI and ML-driven logistics optimization can reduce costs through real-time data and forecasts. For example, a BPA and ML-driven system can optimize routing of delivery traffic, thus enhancing fuel efficiency and reducing delivery times.

 

Machine Learning and BPA: Key drivers for business efficiency

Business process automation systems imbued with AI and ML capabilities expand the potential of automation. By giving these systems the ability to generate cognitive insights – both predictive and prescriptive – machine learning empowers business leaders to make more confident, data-driven decisions.

Further, machine learning integrated with AI-powered automation workflows does more than traditional analytics. The combination of ML and BPA helps enterprises process and leverage unstructured data to predict the outcomes from all possible strategies. But more importantly, it can transform how your IT systems work and interact with each other, with intelligent automation and insights that enable employees to be more efficient.