Solomon Gnanaraj

Yes — AI can play a big role in spotting risks early, often before they escalate into crises. Think of it as an “early warning system” that works faster and at a scale humans can’t manage alone. AI can help in Alternative Investments Transfer Agency BPO domain by identifying risks earlier in several areas where manual processes, High volume, Time sensitive work etc. Transaction Processing Incomplete (NIGO – Not in Good Order) type transactions, Subscription agreements can be identified through AI before they reach final review. This helps Transfer Agent to alert the fund about particular transactions before any cut-off dates. (So the investor won’t lose the opportunity). Investor data and Compliance Risk AI Can validate Investor KYC/AML data and identifying (also alert) any potential fraudulent investor details. Operational Bottlenecks As Alternative Investments clients have different product structures and they have different timelines (trade dates), cut-off dates, event start and end dates (Basically different TAT to be followed for different product structures). Manually monitoring them is a tedious process if we support multiple clients and different funds. AI can help in identifying this early and provide warnings of any possible missing TAT for any particular work type/fund etc. This way we will avoid the risk of missing SLA. Alternative Investments BPO has tax season volumes during the year. Based on the past volume trends, AI can alert about having appropriate staffing for the tax season. This would help the management to staff accordingly. Client sentiment Risk This is one of the important area when we support more number of clients under the same roof. Based on the email interactions, queries, CMP feedback, we can track and tell clients dissatisfaction and risk of losing them. So that Management can take proper proactive actions before they become sensitive.

Ai can make customer interactions feel personal if it combines context, Intent and empathy. It is important to train the AI to understand who the customer is, what they need, and how best to respond in a way that feels natural and humanly. 1. Context Awareness Use customer profiles using data from history. This allows to tailor the responses. 2. Sentiment Analysis AI should detect if a customer is happy, confused or frustrated. Also adjusting the tone, urgency. This makes a sense of being truly heard. 3. Proactive Engagement Instead of waiting for a customer to ask for help, AI can understand the needs. (based on browsing, behaviour AI can provide a support and suggest solution) 3. AI powered summaries for Agent Before a human joins a conversation, AI should provide a quick summary of the customers history and the mood. This would help the human agent to respond with empathy and precision. In Alternative Investments Transfer Agency, this can add more value The Challenge in Alternative Investments domain Investors and CRMs expect quick and accurate answers. But they also want to feel their query is treated uniquely. Generic AI responses feels robotic. Also need to ensure that too much of data or overused customer private data can bring a feel of intrusive. Traditionally, Investor submits the subscription agreement and waiting for the update from CRM/Transfer Agent. 1. Contextual acknowledgement/Responses - Instead of saying a generic response that "your documents are being reviewed", AI can provide a contextual way of response, "your KYC document for fund XYZ is being reviewed. Only Identity proof and foreign Swift code need to be confirmed/verified" along with Turn around Time. This way the customer not only getting a timely response also a clear and precise response. 2. Proactive updates - This way we dont have to wait for Investor to ask for help. Based on the history, AI would be able to provide periodic updates by understanding what he might need to know. 3. Friendly language - can improve the experience for the investors on a particular situation. 4. Seamless Human handoff - When there is an issue, AI can provide all summary to CRM/TA Agent, this way they dont have to ask multiple same questions to investors. It is also bring efficiency. This way, Quick , Personalized, precise responses can be provided to Investors. This brings more trust as they feel they are "known". Investor would feel their case is handled with care, Quick turn around also compliance risk can be reduced.

Yes. AI can turn knowledge into competitive Edge. When automation was first introduced through technologies like Robotic Process Automation (RPA), the primary goal was to achieve efficiency by handling repetitive and rule-based tasks with speed and accuracy. This reduced manual effort and allowed employees to focus on more value-added activities. Over time, organizations realized that automation could go beyond repetitive work. By adding cognitive capabilities such as learning, reasoning, and decision-making, Artificial Intelligence (AI) was introduced into business processes. While RPA can follow the defined rules, AI has the ability to analyze data, understand patterns, and adapt to new situations. As more processes and tasks are handed over to AI, it not only executes them efficiently but also builds knowledge from every interaction. This accumulated knowledge helps organizations make smarter decisions, predict outcomes, and innovate faster. In Alternative Investments Transfer Agency, Poor knowledge management creates waste or risk in complex product processing rules. Each client has different structures, fee rules, Redemption timelines. Team members often has to manually search multiple documents, emails, spreadsheets to find correct instructions. This creates 1. Operational delays – slower processing of Purchases, redemptions and transfers 2. Errors – Following different rules for a particular client creates errors including compliance and financial risk. 3. Training inefficiencies - New team members spend more time learning all procedures. (Long curve of learning) A Prompt and Flow-based AI solutions can transform this 1) Centralizing Knowledge – AI ingests clients documents, emails and other documents properly 2) Guided experience for users – Processors can follow step by step auto populate fields based on AI guided based on the client business rules. 3) Faster turnaround on complex processing tasks 4) Reducing operational risks and errors 5) Higher scalability (seasonal volumes, new fund events can be handled without any issues) 6) Achieving client satisfaction due to reliable service.

Bias In – Refers the Bias that enters an AI system through its inputs (data, design choice and assumption). Unfairness can happen through the way the data collected, missing diversification, choices selected by the developers, also, and some historical data. How do break Bias In? – Collect diverse, representative data, using fairness techniques. Build diverse team to reduce blind spot. Bias Out – This is the output results or decisions based on the biased inputs. This can provide unfair predictions and recommendations. How to break Bias out? – Fairness checks on output, Keep human in decision making, continuous monitoring. In Service Delivery – In my domain Alternative Investments, We have a different product structures for the same client, same type of work. (Private equity, Private credit, Reit funds, Interval funds, etc). This caused issues when AI standardized the rules for New account creation or Redemptions. Also, there are few source files from particular Broker/Dealer needs tax reporting handled by our transfer agency. So, we need to have different series of account types while all others should receive different series accounts. These are the challenges we see today. How to prevent/Overcome? Design – We continue to work with our tech teams to identify these different product structures and use balanced training data across all product structures and account types. Test with all such rules and scenarios. Monitor – there are few steps involving HITL currently to ensure the Bias is addressed. So, our processors manually checking such account types and correcting them if necessary.