Lots of hype surrounds artificial intelligence (AI). So, identifying practical applications that deliver tangible returns is the mission for most.
The good news is that lots of the processes financial institutions believe could be tackled do not need the latest, cutting-edge machine learning. This would be like using a sledgehammer to crack a nut.
A combination of less complicated machine learning capabilities and a rules-based approach is the best answer for the transformation desired. This also means operational users, rather than IT, can define, train and test the services, thereby lowering the threshold to adoption.
Here are a couple of current examples.
Improving fraud detection capabilities
One of our banking clients already uses Xceptor to scan payments to identify fraudulent transactions. Writing rules to capture fraud is complex due to: the size of the data set; it being nigh on impossible to catch every fraudulent transaction; the prevalence of false positives. As fraudsters are relentless in evolving and finessing their approaches, financial institutions need to be as well.
By deploying a combination of both a rules-based approach and native machine learning, we can extend the level of automation in the fraud detection process. We apply hard rules e.g. blacklisting known fraudsters when sufficient and more complex rules are absorbed into the machine learning model where it learns, it can be staged, and it improves. With this approach we can constantly finesse those rules, increase the confidence score and the detection rate.
Classifying intent in emails
Another example is a financial institution that has 1000s of incoming client emails containing either netting or standard settlement instructions. Using natural language processing (NLP)-based machine learning, we can train the model to assess intent.
For example, classifying the emails as either netting or SSI, assessing the right priority and sending to be reviewed by the right team.
Once again, a lot of the tasks in the overall classification process e.g. sending an email to a user to login, do not need machine learning. Deploying simple rules in conjunction with native machine learning enables the broader process to be automated.
The right technology at the right time
Intelligent automation deploys a variety of techniques throughout a process. Machine learning is not a magic answer to automation. Nor is ripping everything out and replacing with AI. Deploying the best technique for each task throughout the process, enables a higher level of automation to be achieved and human intervention to happen as and when it is needed.