As the financial services industry undergoes a digital transformation, artificial intelligence (AI) plays a key role.
As the financial services industry undergoes a digital transformation, artificial intelligence (AI) plays a key role.
While AI will undoubtedly result in job losses, it can also rejuvenate the economy.
There is no doubt that the financial services sector is ripe for more automation as new reports on artificial intelligence (AI) emerge each week.
Automating processes has led to cost savings, improved customer service, and enhanced overall efficiency within banks and financial institutions.
Institutions seek greater automation to improve regulatory compliance and gain an edge over their competitors. Automating operations and functions is now becoming increasingly popular among fintech leaders.
All three of these technologies are ready for deployment. What may be more challenging is identifying where to begin or which services should be automated. A leading fintech firm and a large bank may have an edge. Nevertheless, for small institutions, any repetitive process that eliminates direct human involvement should be the first to be automated. Listed below are the five most automatable financial services processes.
1. Anti-money-laundering analysis
AML processes cost banks a lot of money, and hundreds of analysts perform AML checks. Investigations of post-transaction activities consume billions of dollars a year.
A significant benefit of automation is that it can significantly reduce the time spent pooling data from multiple sources. By automating data collection, collation, and case investigation, workers will spend less time and money and experience less boredom.
Moreover, automated systems can operate at machine speed 24 hours a day, seven days a week, so analysts are not only able to improve output but also engage in other valuable tasks.
2. Know Your Customer (KYC) processes
Financial services processes such as KYC can also be automated. Checking multiple databases is exhausting for your customer service employees. The KYC process takes a lot of time and money. Automating these often repetitive processes using artificial intelligence and robotics shifts the focus from large teams to high costs.
Banks can use automation to employ multi-skilled virtual workers to speed up KYC and reduce costs. The automation of virtual workers also allows them to operate under set rules. The system can alert analysts to sensitive or bizarre cases using this feature. Virtual workers can perform KYC checks during holidays and after-hours, just like in AML.
3. Claims processing
Large teams of insurance companies currently handle the process of reviewing and processing claims. It can be exhausting for workers to review so many claims at once. Claims are often subjectively processed as a result of this.
As a result of automation, claims are processed at a much faster rate as well as fraud is better detected. Data extraction can be automated to increase speed and accuracy.
Documents submitted by claimants can be analyzed using AI-powered systems, contracts can be compared, and irregularities can be flagged.
4. Quote generation
It has been my experience to interact with many people who could not fill out a form for one quote generation or another. It is time-consuming and can result in lost business sales since customers do not enjoy the process.
To solve this problem, machine learning software can be used to automate document processing. The inbuilt technology extracts unstructured data from the customer’s current policy documents. As the information is sorted and processed, it provides quotes based on customer needs, noting differences in pricing.
5. Back-office tasks
Regulatory compliance and customer experience processes aren’t the only processes that can be automated. Virtual workers can handle various back-office tasks within automated systems as they are process-agnostic. An aspect of this can boost a company’s workforce and increase productivity by alleviating the burden of too much work across systems.
Software bots can perform many tasks, including re-entering data, sending emails, downloading files, managing client accounts, generating statements, reconciling trades, onboarding new employees, and managing data reporting.
Conclusion
By automating operations previously reliant on manual labor, companies can optimize them. Both institutions and clients benefit from the additional cost- and time-saving aspects. By 2030, AI implementation could reduce operating costs in the global financial sector by 20-40%.
