Hyperautomation - not a magic wand but indeed a super tool

Almost all the business sectors have been seeing the increasing use of AI and RPA, especially so in the case of financial services sector. It is predicted that banking will be one of the top sectors spending on AI by 2024. Hyperautomation enables this sector to address challenges including a growing consumer preference for digital banking channels and the increased competition posed by fintech startups. Some of the areas where hyperautomation will have a major influence are: Automation of tasks by using robotics and minimizing manual efforts while maintaining the effectiveness of the solutions. Integration of AI and ML is key in automating the extraction of structured data from unorganized data sources.

Hyperautomation can be a very effective tool in customer onboarding and KYC processes, loan origination and credit appraisal fraud detection and prevention and compliance reporting.

Customer experiences can be improved manifold using data and automation, such as RPA bots and streamline tasks like document verification and risk assessment. This makes the onboarding process a cakewalk.

RPA tools can provide predictive insights to improve customer service. Data analysis can be done using AI and ML. Also these tools can be effective in monitoring transactions, detecting potential frauds and enhancing risk management.

Using tools available in hyperautomation like AI, RPA and biometrics, institutions can streamline KYC and customer onboarding. The processes can automate data extraction, document verification and risk assessment for compliance. AI algorithms can verify the authenticity of documents and cross-reference them with databases to check for discrepancies or fraud.

Hyperautomation can accelerate loan processing by automating the collection and analysis of applicant information and make loan approvals faster and risk assessment more accurate.

Regulatory reporting can be totally automated - collecting and validating data and transforming it from various sources.

Institutions can use hyperautomation tools to analyze customer data, enabling the creation of tailored marketing campaigns and product suggestions.

Hyperautomation can enable systems to learn and adapt based on data inputs. By using AI and ML algorithms, institutions can identify patterns and trends in data that may not be immediately apparent, allowing for more accurate decision-making.