Gopu Nair

In many sectors, employee onboarding processes have quite a few similarities across geographies, baring difference with respect to regulatory compliance, overall industry requirements etc. AI can be leveraged driving greater standardization in such processes by parameterizing key steps related to compliance and risk assessment. The Onboarding Process being the first touchpoint for any employee joining an organization somehow lays the expectation for the employee regarding the underlying culture and focus on seamless process driven nature of the organization. Through AI-Enabled processes we can implement a standardized framework for onboarding by automating core activities like background checks and a scoring mechanism based on algorithms mapping the digital footprint of the individual and his/her association with various forums & communities online. Validation of supporting document and certificates with AI-powered OCR and fraud detection, ensuring quality and mitigating of risk related to false educational records and work experiences. With the AI system working autonomously, compliance with data protection and privacy parameters can be in-build with limiting the requirement of human intervention only for resolving errors or anomalies appearing in the process of verification or validation of records/data provided by the individual. The process set to work in unattended mode would forgo the human agent bias and misses which may go unnoticed at times due to want of focus and consistency in deploying effective controls and checks in the process. Training the model over with sizeable number of transactions with fair set of cases with anomalies, so as to enable the system to detect these accurately rather than highlight false positives that would strike a fair balance between quality and accuracy, wherein accuracy is of outmost importance.

The chosen process is Investor support services in Primary markets. During IPOs the transaction volumes are at times unpredictable due to various factors especially market conditions and global financial movements. Furthermore, the sector being highly regulated and transactions to be executed in timely manner calls for fine balance between robust processing and adaptability to changing Regulations . An AI agent handling Investor queries must flexible in design to adapt to these changes to remain effective without requiring a complete rebuild. Designing an Adaptive AI Agent The primary requirement of the AI agent would be to always be aligned with the evolving process. For this propose a modular, learning-based architecture with continuous feedback loops would be most suitable. - Modular Architecture Breaking down the entire process into units and developing solutions around the activities would ensure that process changes are incorporated with minimal efforts without impacting the overall solution. - Continuous monitoring and feedback Since the AI solution is handling critical function, the responses and actions needs to be monitored and error identified and corrective actions taken immediately to ensure minimal or no complaints or escalations are triggered. Also as a best practices it would prudent to mention that the transactions is/was processed by a Bot and in case if the recipient notices any error to kindly highlight the same immediately. Therefore, seeking timely feedback ensures that the solution meets its purpose of reducing human efforts rather that spending more time in making the solution work effectively. - Change management With changes in Regulatory environment and Industry standards, these have to be incorporated into the process and AI solution to ensure relevance and usefulness. This can be achieved through engagement with Regulatory bodies and Industry experts to foresee changes and anticipate the timelines of these changes having an impact on the process. - Stability of the solution Though the solution would be able to handle volumes however, like any technology intervention needs to have a fall-back mechanism to ensure the process is not stalled due to any reasons. Since being a modular Architecture the component failing can be isolated and work around can be deployed to ensure continuity. A Human-in-the-loop mechanism would prove to be an additional monitoring mechanism along with ensuring that the AI model is trained and performing the tasks as planned. This can also be utilised as a fall-back mechanism in certain scenarios. Overall the solution could prove to be an effective model as it leverages the existing applications and technologies due to its modular architecture and provides flexibility to incorporate changes in structured manner with least impact to production output and thereby being cost effective.

In the Financial services domain, KYC update is the easiest as well as critical process especially if its related to monetary payouts. With Regulatory bodies clearly defining the requirements and controls to be built in a process, the shear volumes of transaction and the inherent pressure to complete the task within stipulated timelines can and may lead to oversight resulting in erroneous payout and in some instances deliberate financial frauds. By creating an automated system of capturing key data sets from supporting documents submitted during KYC, these can be mapped to an individual's other financial footprints e.g. usage of same bank account elsewhere in ecommerce or other platforms. Creating a user profile with the KYC document and comparing the trends or digital footprints of that user could help identify early warning signs and flag off that particular transaction. The profile can also be shared with peers in the industry using a secure database format to have search their own database for holdings of such individuals and set up early detection based of past trends. Since this entire process being automated with least or no human intervention, the model would be devoid of bias or create false positive response.

Traditional Work Force Management calculation are done basis TAT for an activity and the number of resources required to complete the task within a set time. Anomalies such are work volumes, inherent process complexities and at times shrinkage are either not accounted for or are simply overlooked. Thus creating a work overload on the present work force by way of stretched workhours. AI would assist the managers by help plan resource requirement basis predictive models created by referencing historic productivity figures, skills set and even include anticipated volumes of tasks. With a holistic view point through AI enable WFM solution organization would be in a better position to plan and optimize the output and thereby build an efficient workforce.

Though the service industry has been defined by standards sets by humans and responses rated based on the overall experience in handling the query, with AI capabilities particularly NLP the engagement levels have increased with hyper personalisation. AI systems are able to ask pointed questions and provide specially customised response in the manner the query was asked by the customer. With a feedback mechanism these AI response are checked for effectiveness and help build layers over the Knowledge base and create historical references. Effectively the next time a similar query is asked the response time is reduced and the script can be hyper-personalised thereby prompting positive sentiment and overall satisfactory experience.