Technologies are the benchmark for any businesses. The power of IoT and Analytics are redefining the process and the combat is successful at the trial. Machine Learning, Natural language processing, and Robert automation process these are something best industries can have. You go with any of them with the specification to your business and the results are beyond imagination. These technologies can automate operational loss with better efficiency and accuracy. Banks can enable and avoid such operational loss through automation rather than manually.
Banks have a various process to manage their internal and external data. Let’s talk about this, data coming from various sources and now handling them manually with business lines, event types, risks, and so on, it’s really complicated. Handling manually may increase the chances of human errors. At least automation would decrease that with less loss of data.
This where Machine learning can help them by automating these processes this would make humans the really smart and focusing more on something productive. We have to train the system for all these processes and then look out at the benefits.
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Get Data prepared
We have to take care of the data by keeping sure it should not be less, I mean there should be various scenarios or incidents on data processes where we can train the machine to exactly understand what is required. Loss database would lead us to poor results and data preparation will help in proper data mapping.
Creation of Model
During this stage, the number of times a word appears in the incident description is been checked. This could be an English word which is either be removed or included while mapping activity. This identification can help us to identify which one can be used for mapping an incident risk, business line, event type, and etc. So, we have to train the machine in such a way that it should identify which word to include or which to exclude for mapping purposes. So, based on this frequency of appearing words, a word cloud is generated to set them accordingly.
Validation and Deployment
Now once the machine has gained the level of accuracy and efficiency it is fully validated against the dataset for different parameters of the model. Though the level of accuracy depends from bank to bank but once achieved it is fully validated.
Hence Machine learning can help us from operational loss, there is a lot of daily activities that industries have to deal with. Things like quality reports, limited resources, stakeholder’s management, investors, etc. This is the time industries need to focus on the automation and reduce the pain they are bearing somewhere in the process. This will help them to defend the common issues faced and overcome them with new business capabilities and models.
Thus, operational loss taking place wouldn’t be a crunch so more and this would lead us to human profitability with more production values and results. These are just a few points we can start considering with the ocean of innovation and technology has much more.