Cognitive Automation Tools: A Brief Overview
Smarter data management is a major necessity for banks to operate efficiently and spur expansion. We now have a set of tools thanks to cognitive technologies that can mine massive amounts of complicated data from video, audio, text, and structured databases to gain insightful knowledge that will help us make decisions. These technologies are hot now thanks to new artificial intelligence (AI) developments that have improved speech recognition, computer vision, machine learning, natural language processing, and robotics. Cognitive tools evaluate and extract meaning from the unstructured text using a variety of algorithms that are created utilizing probabilistic logical reasoning, neural network-based architectures, fuzzy knowledge representation schemes, and more.The following are some examples of how banks employ cognitive tools:
Profiling and Personalization
The various sorts of data that a bank frequently gathers can provide information about its customers' personalities, lives, preferences, and aspirations. To increase engagement and find cross-sell and up-sell opportunities, leverage these insights.
Profiling is helpful when dealing with business clients. Cognitive technology can compile online data on corporations and create profiles that are relevant to the query's context. When a bank processes a loan application, for instance, an AI-based application can retrieve previous loan requests made by the customer, if any, from the database, as well as the client's credit score, borrowing history, and repayment information from regulatory filings. Social media opinions about the company regarding this specific component may also support this, helping to create a comprehensive profile relevant to the loan request. Financial advisors, in turn, make use of these profiles. There are also plans for new predictive models that can profile customers based on cognitive inputs.
Cognitive assistants for individualized interactions: With the development of natural language processing, banks are now using chatbots to have discussions with customers similar to a human. This is useful when a customer must sort through several selections or complicated technicalities on a bank portal. Bots provide both the feeling of self-service and individualized support. Customers may stay in touch with banks around-the-clock thanks to technology.
Banks can also look into hybrid systems, which let a bot handle some of the customer services until a human agent takes over to provide more individualized responses. Additionally, bots can proactively broadcast to users customized information about financial services. In this case, the bot must be educated on reliable data sources. For instance, the chatbot should be taught how to respond to any questions a consumer might have about a good or service that it is meant to support. The bank's customer interactions should be carefully planned. This puts bank employees in the customer's shoes and is a useful technique to comprehend their pain areas.
The more interactions customers have with conversational systems, the more data is accessible for machine learning algorithms to learn from, construct, and improve client psycho-demographic profiles. These profiles aid in offering a rich, tailored experience that can slickly combine a person's long-term behavioral traits with their immediate or short-term needs.
The real-time detection of regulatory infractions is a relatively recent application of cognitive technologies. Given that infractions result in stringent regulatory scrutiny and severe penalties, this might prove to be a competitive advantage. Of course, this requires that the application be designed with the ability to analyze compliance standards and regulations hidden within unstructured documents deeply. The arduous task of keeping track of modifications and exceptions is now being automated by clever algorithms that combine deep learning with conventional machine learning techniques. While there is evidence that these algorithms benefit from human annotations, efforts are being made to determine whether there are more effective ways to learn from observations of human activity.
The too much data, too little insight issue that has long plagued banks has a solution in cognitive computing. Decision-making on routine inquiries, fraud detection, and rich customer experiences are just a few examples of how data may be used at an entirely different level. Despite the cost and training associated with cognitive tools, the advantages sometimes come as a pleasant surprise.
Tools for Classical Automation Versus Cognitive Automation
Along with comprehending the complexity of technology, keeping up with the tongue-twisting terms is a difficulty given the light-speed advances in ML/AI technologies every few months. Even while all or nothing may not be the most realistic solution for certain firms, these technologies are often buried in larger software suites, which only worsens the situation.
Let's examine how cognitive automation fills in the gaps left by less effective forms of automation, most notably robotic process automation (RPA) and integration tools (iPaaS).
Traditional RPA is primarily limited to automating tasks that require quick, repeated operations without considerable contextual analysis or handling eventualities (which may or may not involve structured data). In other words, the automation of business processes they offer is primarily restricted to completing activities according to a strict set of rules. Because of this, RPA is sometimes referred to as "click bots," even though most applications nowadays go well beyond that.
Automated systems can work well if the decisions are made according to a "if/then" logic without requiring any human judgment in between. However, this rigidity prevents RPAs from processing forward unstructured material and retrieving meaning.
Variants of Cognitive Automation
As mentioned above, cognitive automation is fueled through the use of machine learning and its subfield, deep learning in particular. And without making it overly technical, we find that a basic knowledge of fundamental concepts is important to understand what can be achieved through such applications.
Machines are already very good at a few activities, even if we are still far from what scientists call broad artificial intelligence. In reality, with the right training, computers can duplicate our skills and frequently beat people in terms of speed and accuracy:
● Visual computing (also referred to as image processing)
● Character recognition using optical (OCR)
● Processing language naturally (NLP)
● Sound editing
We won't get too deeply into the specifics of machine learning here, but if you're curious and want to learn more, check out our introduction to how computers learn.
Now that some of them have been contextualized let's focus on two instances where cognitive automation has been able to rethink labor processes and content.
Process of Claiming
One of the labor-intensive jobs that insurance company staff may have to perform is processing claims, which has an operational impact on the business. Many of them have adopted cognitive automation solutions to optimize this difficulty significantly.
Most of the critical routine procedures involved in claims processing can be automated using cognitive automation. These instruments can transfer client information from claims forms that have already been completed into your customer database. Additionally, it can scan, digitize, and transfer client information from printed claim forms that would typically be reviewed and processed by a human.
Automated Document Processing
As mentioned above, the secret to cognitive automation tools' effectiveness lies not only in carrying out the system's rules but also in converting unstructured files, such as documents, into structured data: This data can finally be merged with the remaining systems landscape by removing pertinent unstructured data from them and translating it into a standardized format.
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