Today I'm going to discuss on a topic called anti-money laundering, which is very important in banking and financial institutions. Let me bring my presentation doc. So today we are gonna discuss in detail about cloud-based anti-money laundering implementation using nice optimize tool. Okay. My name is Vi Kumar. today context is majorly we'll explore how the implementation of cloud-based and a ML solution has former compliance operations at a major financial institutions helping its streamline regulated adherence, reduce cost, and improve investigation of CNC. High level. This case study will discuss the challenges faced by financial institutions, the adoption of a closed based solution. The modules implemented the data migration process and the benefits related to post implementation compared to the traditional systems. The need for change. First of we, we'll understand what is the problem with, the legacy, the traditional systems. Then we walk, we'll walk through you on the benefit of a ML using nice optimiz, tool. Okay? The global regulatory demands are becoming increasingly complex. Regulatory bodies are introducing Know your customer, KYC protocols, due diligence checks, and expanded reporting requirement to come back. Financial claims such as money ing and. Financial institutions must add ahead to these regulations across multiple jurisdiction, each with its own compliance standard and the framework with the legacy systems. Traditional A ML system often rely on outdated technology creating. Silos of fragmented data and involves cumbersome manual processes, meaning that even though this world legacy systems are there, basically this is not competing with the latest times which is happening and the financial banking system. These systems frequently generate high volume of false positive, burdening compliance teams with unnecessary investigation. Furthermore, the systems struggle to adapt to emerging financial pre tactics and the rapid phase of regulatory change and bring and effect. New threats. This is what we understand why we need to change our, what are the problem with the traditional legacy applications. That's where we got a nice optimize cloud-based a ML system to overcome the problem, which is facing by legacy systems. I will give you an overview. What exactly ni NICE Tool NICE is a leader in financial crime and compliance solutions, which is strong track record of innovating technology and the expertise in financial regulations. It's often next generation cloud-based platform tailored for financial institutions. To me the demands of modern A ML complaints, key modules, suspicious activity monitoring. So majorly this, nice act, key modules, suspicious activity monitoring. Continuously analyze transaction in real time to detect suspicious patterns. Money ing or fraud watch list filtering integrated with global sanctions and the regulatory list screaming transaction. A known individuals companies are countries with. Gather and assess information from clients to evaluate the risk levels, helping financial institutions assess the potential for fraudulent or illegal activity. Now, obviously to do this all the thing for anything, we needed data. We needed data. That's where we are first focusing on data integration and the standardization. So data sources. Obviously for any investigation, we needed data, so that's what we used to call data sources. Successful integration of data from what tool? Critical sources such as code banking systems, high frequency trading platforms, and the global payment networks. The integration helps to create a consolidated, comprehensive view of customer under transactional data enhancing complaints, capabilities, standardized data model. Now you are extract the data from different sources. Now it is there in a single platform. Now we need to. Unified schema was designed to harmonize over 1 million transactions per day across multiple data sources. This model ensures the consistent data formatting reduces latency, and the facility is faster and more acuity analytics. Critical data points over 200 essential data points, including customer profiles. Transaction patterns and risk indicators are integrated to create robust complaints framework. This mapping has in identifying suspicious activity, ensuring complaints. And the future proofing are gonna changes in the regulation. So this model is helping to integrate the data from different sources, which is required for banking or financing, institute regulations, standardized data model, using a unified data schema and critical data points, which is. Ensuring compliance in the banking system. There's the data integration understand model will help for us. Next benefits of cloud, cloud implementation. Now majority of the applications are moving towards cloud because of there are some advantages. For implementing nice active solution in the cloud come rather than on-prem implementation. Reduce infrastructure cost by migrating to the Clay Cloud. The Financial Institute eliminate $2.4 million in annual hardware maintenance. Data center print by 60%, 60% the cost they're saving by moving this a ML applications into cloud. Look at the benefit 60% they're saving in their. This reduction in cost enable the organization to allocate resource more efficiently and invest in strategic complaints in C two. Now improve based upon the volume in all offerings, a concept. The more you are getting the data volume you can add to scale, meaning that at the time you can ask more resources, more computation resources, so that once it is processed, then it can, you can, bring the down the cost up. You are hardware and processing cost. That's the meaning of scalability. Cloud implementation, significantly reduced system maintenance by 75%. So they are taking care of all your maintenance activity. You don't require to bother about that one. You only focus on your business logic. Rest, cloud, cloud providers taking care of that. Coming to advanced and suspicious activity detection. Basically our OPTIMIZ and NICE Ed AM system is integrated with the four 15 global regulatory watch list, enabling instance, cleaning of transactions. say it is like a sniper, it'll go and monitor what is happening on the transactions. This ensures immediate detection of potential risk and enhance the institution ability to meet regulatory compliance standards. Again, advanced analytics. The system uses ML algorithm them to process and analyze millions of transaction daily, and it can detect the sophisticated ing patterns out of the data. So that you can focus on most critical investigations. Most of critical investigation reduce investigation time. By automating the prioritization of our, and streamlining the everyday gathering process. The solution reduce. Regulat time by 50% allowing compliance aim to focus on the most critical investigation, custom scenario development, the institution developer lower 75 custom detection scenarios tailored to its business model and the regulatory environments. So now our NICE am L system application. So they, I adminis the study. Most of the regulation, they made very robust so that it can tailor a particular country or particular financial I misute requirements. So it is so much robust solution. Okay. So you can plug and play anywhere you. Scenario testing Each detection scenario underwent rigorous testing to ensure the acute greatly identify suspicious behavior while minimizing false positives. This validation process ensures high levels of compliance accuracy now continuous. Money laundering tactics and regularity changes. The scenarios are regularly updated, feedback from analysis systematically collected, and the use to refine detection capability. See now today our nice optimize solution can face all the challenges, but tomorrow this money. We take the feedback from the different regularity authorities and we keep updating our system so that it can meet the financial and banking regulatory complaints. That's the meaning of wherein our NICE optimize is having capability to improve the latest and greatest challenges, which are. In the system. Okay, now post implementation system availability. So the financial institution achieved 19 9.99 uptime for a ML system ensuring that no compliance monitoring gaps exist. This is IE availability supports real when the system is available. 99. So it can capture real time fraud while monitoring the system, and immediately it can give alerts so that banking agents or financial issues can take necessary actions on that one. Automated reporting or 75% of reports are already automated, and, with a few, based upon the regularity, the small harassment, they have to take care. User interface. The redesign user interface boosted analyst productivity by 40%. It features one click access to essential data, customizable dashboards, and in intelligent workflow automation, making compliance task faster and easier to manage. The solution reduce false sellers by 40%, meaning that I, so even though the transaction is really a legitimate transaction, but the traditional world Legacy a ML system used to give false positive pulse positive analyst, our compliance team. On that, transaction and, finally they found it's a legitimate, it's a good transaction. So it's all our, a ml, I say a ML system is taken care to remove the pulse positive alerts so that it is saving 40% off the compliance team time. Was improved by 60% through Advanced ml, enabling the system to uncover complex money handling patterns more effectively. Basically now, 60% whatever really, fraud transaction out of hundred 60% of the transactions are really, it's a transactions where our NICE is. Infrastructure Cloud optimize resulted in 35% reduction in infrastructure cost, while also improving systems scalability and performance implementation. Speed. Full implementation was kept, completed in eight months, four months ahead. The industry standards demonstrating the efficiency of the. Says Bio A ML implementation. S partnership. Faster collaboration between business units compliance and IT team through regular training committee and to integrate with a ML system data standardization wherein establish strong data governance framework and. Standardizing data models to improve consistency across systems leading to more accurate analytics and improve regulatory monitoring, continuous improvement. Already we discussed on that one. So a proactive approach is essential. A proactive approach is essential. Scenarios, compliance process and technology performance help to adapt to emerging risk and ensure compliance with evolving regulation. What is meaning? Meaning that there is a change. There is a risk. this money will come with. Money illegal, transfers, relink, transactions. So we are always practically monitoring and adapting to the challenges implementation roadmap. Begin by assessing your current a ML system and work with Niel. Optimize to create a tailored pro migration strategy. This approach, and ensure that your institution can realize similar benefits such as improving detection, accuracy, and reduce pulse past use consultation. Schedule a consultation with nice expert to learn how you can improve your compliance operations while ensuring regularity and operational excellence. Conclusion. Thanks audience for attending my talk on the A ML system implementation using NICE act Optimiz tool, which is very robust and cloud-based. thanks.