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DESCRIPTION:---------------------------------------------------------------
 \n\n🔹 Speaker 1: Swapnil Awasthi\n\nBio (Short): ( 11:00 AM - 11:45 AM)
 \nSwapnil Awasthi is a senior credit risk and analytics professional with 
 10+ years of experience in fraud detection\, ML\, and financial systems\, 
 having worked at Barclays\, Citibank\, and more.\n\nTopic:\nDetecting Synt
 hetic Identity Fraud Using Machine Learning and Behavioral Analytics\n\nSu
 mmary:\nExplains how synthetic identity fraud works and demonstrates ML-ba
 sed approaches using behavioral and transactional data to detect it\, whil
 e addressing governance\, bias\, and regulatory challenges.\n\n11:45 AM - 
 12:00 PM Break\n----------------------------------------------------------
 -----\n\n🔹 Speaker 2: Seetaram Rao Rayarao\n\nBio (Short): ( 12:15 PM -
  1:00 PM)\nSeetaram Rao Rayarao is a Vice President and Senior Lead Engine
 er with 18+ years of experience in cloud computing\, microservices\, and e
 nterprise AI. He specializes in scalable AI systems using LLMs\, Retrieval
 -Augmented Generation (RAG)\, and agentic architectures. He is a Senior Me
 mber of IEEE and a member of ACM.\n\nTopic:\nA Scalable Knowledge Integrat
 ion Framework for Enterprise LLMs: From Research to Production-Grade RAG S
 ystems\n\nSummary:\nThis talk presents a scalable framework to integrate f
 ragmented enterprise data with LLMs. It covers building production-grade R
 AG pipelines\, aligning structured and unstructured data\, and improving c
 ontextual accuracy using semantic and ontology-driven approaches—bridgin
 g research with real-world enterprise deployment.\n\nVirtual: https://even
 ts.vtools.ieee.org/m/555045
LOCATION:Virtual: https://events.vtools.ieee.org/m/555045
ORGANIZER:lavavandrangi@ieee.org
SEQUENCE:22
SUMMARY:Technical Talks -2 Section 
URL;VALUE=URI:https://events.vtools.ieee.org/m/555045
X-ALT-DESC:Description: &lt;br /&gt;&lt;p data-start=&quot;510&quot; data-end=&quot;801&quot;&gt;&amp;nbsp\;&lt;/p
 &gt;\n&lt;hr data-start=&quot;803&quot; data-end=&quot;806&quot;&gt;\n&lt;h2 data-section-id=&quot;15t4t5p&quot; dat
 a-start=&quot;808&quot; data-end=&quot;842&quot;&gt;🔹 Speaker 1: Swapnil Awasthi&lt;/h2&gt;\n&lt;p data
 -start=&quot;843&quot; data-end=&quot;1091&quot;&gt;&lt;strong data-start=&quot;843&quot; data-end=&quot;859&quot;&gt;Bio (
 Short): ( 11:00 AM - 11:45 AM)&lt;/strong&gt;&lt;br data-start=&quot;859&quot; data-end=&quot;862&quot;
 &gt;Swapnil Awasthi is a senior credit risk and analytics professional with 1
 0+ years of experience in fraud detection\, ML\, and financial systems\, h
 aving worked at Barclays\, Citibank\, and more.&lt;/p&gt;\n&lt;p data-start=&quot;1093&quot; 
 data-end=&quot;1194&quot;&gt;&lt;strong data-start=&quot;1093&quot; data-end=&quot;1103&quot;&gt;Topic:&lt;/strong&gt;&lt;
 br data-start=&quot;1103&quot; data-end=&quot;1106&quot;&gt;&lt;strong data-start=&quot;1106&quot; data-end=&quot;1
 192&quot;&gt;Detecting Synthetic Identity Fraud Using Machine Learning and Behavio
 ral Analytics&lt;/strong&gt;&lt;/p&gt;\n&lt;p data-start=&quot;1196&quot; data-end=&quot;1447&quot;&gt;&lt;strong d
 ata-start=&quot;1196&quot; data-end=&quot;1208&quot;&gt;Summary:&lt;/strong&gt;&lt;br data-start=&quot;1208&quot; da
 ta-end=&quot;1211&quot;&gt;Explains how synthetic identity fraud works and demonstrates
  ML-based approaches using behavioral and transactional data to detect it\
 , while addressing governance\, bias\, and regulatory challenges.&lt;/p&gt;\n&lt;p 
 data-start=&quot;1196&quot; data-end=&quot;1447&quot;&gt;&lt;strong data-start=&quot;1494&quot; data-end=&quot;1510
 &quot;&gt;11:45 AM - 12:00 PM Break&amp;nbsp\;&lt;/strong&gt;&lt;/p&gt;\n&lt;hr data-start=&quot;1449&quot; dat
 a-end=&quot;1452&quot;&gt;\n&lt;h2 data-section-id=&quot;1ohqoqo&quot; data-start=&quot;1454&quot; data-end=&quot;1
 493&quot;&gt;🔹 Speaker 2: Seetaram Rao Rayarao&lt;/h2&gt;\n&lt;p data-start=&quot;1494&quot; data-
 end=&quot;1829&quot;&gt;&lt;strong data-start=&quot;1494&quot; data-end=&quot;1510&quot;&gt;Bio (Short): ( 12:15 
 PM - 1:00 PM)&lt;/strong&gt;&lt;br data-start=&quot;1510&quot; data-end=&quot;1513&quot;&gt;Seetaram Rao R
 ayarao is a Vice President and Senior Lead Engineer with 18+ years of expe
 rience in cloud computing\, microservices\, and enterprise AI. He speciali
 zes in scalable AI systems using LLMs\, Retrieval-Augmented Generation (RA
 G)\, and agentic architectures. He is a Senior Member of IEEE and a member
  of ACM.&lt;/p&gt;\n&lt;p data-start=&quot;1831&quot; data-end=&quot;1959&quot;&gt;&lt;strong data-start=&quot;183
 1&quot; data-end=&quot;1841&quot;&gt;Topic:&lt;/strong&gt;&lt;br data-start=&quot;1841&quot; data-end=&quot;1844&quot;&gt;&lt;s
 trong data-start=&quot;1844&quot; data-end=&quot;1957&quot;&gt;A Scalable Knowledge Integration F
 ramework for Enterprise LLMs: From Research to Production-Grade RAG System
 s&lt;/strong&gt;&lt;/p&gt;\n&lt;p data-start=&quot;1961&quot; data-end=&quot;2299&quot;&gt;&lt;strong data-start=&quot;1
 961&quot; data-end=&quot;1973&quot;&gt;Summary:&lt;/strong&gt;&lt;br data-start=&quot;1973&quot; data-end=&quot;1976
 &quot;&gt;This talk presents a scalable framework to integrate fragmented enterpri
 se data with LLMs. It covers building production-grade RAG pipelines\, ali
 gning structured and unstructured data\, and improving contextual accuracy
  using semantic and ontology-driven approaches&amp;mdash\;bridging research wi
 th real-world enterprise deployment.&lt;/p&gt;
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