Dennis Kyalo
Role
Structured Credit Trader
Specialization
ABS Consumer Loans
Location
New York, NY
FINRA
Series 7  ·  63  ·  SIE
Dennis Kyalo
Structured Credit Trader
ABS Consumer Loans  ·  Whole Loan Trading  ·  Structured Products

I specialize in ABS Consumer Loan Trading, focusing on the analysis, pricing, and trading of consumer loan portfolios. I leverage loan-level analytics, collateral surveillance, cash flow modeling, and credit performance analysis to evaluate investment opportunities and support structured credit trading decisions.

Beyond the trading desk, I am passionate about building AI-powered tools that make financial analysis faster, clearer, and more actionable.

View Featured Work Connect
Professional Highlights

By the Numbers

💰
$1M+
Annual Financing Cost Savings
Improved trading desk workflows through analytics, automation, and portfolio surveillance.
📊
Structured Credit
ABS Consumer Loans
Specialized in the buying, selling, and securitization of consumer loan portfolios.
🤖
AI for Finance
Built an RMBS Analytics Platform
AI-powered platform for mortgage analytics, natural language querying, SQL generation, and interactive dashboards.
📈
Quantitative Analytics
Time Series Forecasting Platform
Built a multi-model forecasting application for macroeconomic and interest rate analysis.
🏛️
FINRA Licensed
Series 7  ·  Series 63  ·  SIE
Licensed securities professional with a background in Financial Engineering and Data Science.
My Approach

Discipline Meets Curiosity

I believe success in structured credit comes from combining disciplined credit analysis, market awareness, and sound risk management. Every trading and investment decision begins with understanding the underlying collateral, market dynamics, and relative value. Beyond the trading desk, I enjoy exploring how technology, including AI, can streamline research, uncover deeper insights, and make complex financial analysis more efficient.

Professional Experience

Citigroup Global Markets
Structured Credit Trader  ·  ABS Consumer Loan Desk
New York, NY  ·  Feb 2023 – Present  ·  ● Current
  • Evaluate, price, and trade consumer loan portfolios by analyzing collateral quality, relative value, expected cash flows, and market dynamics across the ABS market.
  • Develop cash flow assumptions and perform credit scenario analysis using market comparables to assess yield, WAL, prepayment behavior, credit losses, and expected investment performance.
  • Monitor originator performance and forward-flow programs through ongoing surveillance of loan performance, portfolio activity, and market developments to support trading decisions.
  • Designed and implemented a payment processing and reconciliation workflow that streamlined portfolio cash operations, contributing to over $1M in annual financing cost savings while improving operational efficiency and accuracy.
  • Develop proprietary analytics, reporting tools, and portfolio monitoring dashboards that improve trading workflows, collateral surveillance, and decision-making across the desk.
The United Nations
Data and Statistics Research Intern
New York, USA  ·  Sep 2022 – Dec 2022
  • Conducted comprehensive quantitative and qualitative research utilizing R and Python, analyzing large-scale international datasets to support global development initiatives and policy recommendations aligned with the UN Sustainable Development Goals.
  • Performed statistical analysis on international survey data to extract actionable insights, enhancing the relevance, accessibility, and utility of the Gender Snapshot report.
  • Contributed to the research, drafting, and dissemination of the 2022 Progress Report on the Sustainable Development Goals, strengthening the publication's data-driven foundation and supporting evidence-based international advocacy.
Central Bank of Kenya
Research and Statistics Intern
Nairobi, Kenya  ·  Oct 2019 – Mar 2020
  • Developed time series models on large structured and unstructured financial datasets from 40+ financial institutions nationwide, enhancing economic policy analysis and decision-making capabilities.
  • Collaborated with senior analytics teams to optimize machine learning and econometric models, improving inflation rate predictions and market trend forecasting accuracy by over 20%.
  • Automated statistical analysis and reporting of critical financial datasets using R Markdown and built dynamic R Shiny applications to streamline data compilation, analysis, and visualization for senior policymakers.

Featured Work

Structured Products  ·  Mortgage Analytics  ·  AI

RMBS Origination Analytics Platform

An intelligent research and analytics platform that enables structured finance professionals to conduct AI-assisted research, analyze residential mortgage origination data at the loan level, generate interactive visualizations, and answer complex questions through natural language.
Live Demo ↗
RMBS Origination Analytics Platform, AI research interface with featured prompts for mortgage collateral analysis
RMBS Origination Analytics Platform, natural language research and loan-level mortgage analytics.
The Challenge
Residential mortgage origination datasets contain millions of loan-level observations across vintages, sellers, geographies, borrower characteristics, and collateral attributes. Extracting meaningful insights typically requires writing SQL, manually aggregating data, building visualizations, and interpreting results, making research both time-consuming and highly technical.
The Solution
Built an intelligent research and analytics platform that allows structured finance professionals to ask complex questions in natural language. The platform retrieves relevant context, generates SQL automatically, performs loan-level analytics, creates interactive visualizations, and delivers structured research outputs instantly.
Data Coverage
Loan-level residential mortgage origination data spanning multiple origination years, covering seller, vintage, geography, FICO, LTV, CLTV, DTI, interest rate, occupancy, loan purpose, property type, and original balance.
01
Natural Language Research
Ask complex questions in plain English and instantly retrieve loan-level analysis, interactive visualizations, and structured research insights, without writing SQL.
02
Loan-Level Analytics
Analyze residential mortgage origination data across seller, vintage, geography, borrower characteristics, and collateral attributes through automatically generated analytical queries.
03
Vintage & Seller Analysis
Compare origination cohorts and seller performance across FICO, LTV, CLTV, DTI, interest rate, original balance, and other key credit characteristics.
04
Interactive Visualizations
Generate publication-quality charts and dashboards that help identify portfolio trends, compare collateral characteristics, and support structured finance research.
05
AI Research Summaries
Automatically generate structured analytical narratives that summarize key findings, explain trends, and present insights in a format familiar to structured finance professionals.
Time Series Forecasting  ·  Macroeconomics

Economic & Interest Rate Forecasting Platform

An interactive time series forecasting platform for analyzing and predicting U.S. economic and interest rate trends using multiple statistical forecasting models.
Live Demo ↗
Economic Rates Forecasting Platform, interactive R Shiny dashboard showing U.S. Inflation Rate forecast with Auto ARIMA model and confidence intervals
Interactive dashboard for forecasting U.S. economic and interest rate series using multiple statistical forecasting models.
The Challenge
Economic and interest rate forecasting often requires evaluating multiple statistical models to determine which performs best for a given time series. Comparing model performance, visualizing forecasts, and interpreting results can be time-consuming without a unified analytical workflow.
The Solution
Built an interactive forecasting platform that compares multiple time series models, evaluates forecasting accuracy using standard performance metrics, and visualizes historical data alongside future projections to support economic analysis and decision-making.
Auto-ARIMA
Automatic ARIMA model selection. Optimal for stationary and near-stationary rate series with autocorrelation structure.
ETS
Error, Trend, Seasonality decomposition. Handles trend and seasonal patterns in rate data with state-space smoothing.
Prophet
Decomposable time series forecasting model designed for business and economic data with changing trends, seasonality, and missing observations.
TBATS
Handles complex seasonality. Particularly effective for series with multiple seasonal patterns and irregular cycles.
Forecast Accuracy Metrics
RMSE
Root Mean Squared Error
MAE
Mean Absolute Error
MAPE
Mean Absolute Percentage Error
MSE
Mean Squared Error
Rate Series Covered
·10-Year Treasury Yield
·30-Year Mortgage Rate
·Federal Funds Rate
·CPI Inflation
·3-Month Treasury Bill Rate
01
Multi-Model Comparison
Compare forecasts from Auto-ARIMA, ETS, Prophet, and TBATS side-by-side to identify the best-performing model for each economic time series.
02
Interactive Forecast Charts
Visualize historical observations, future projections, and confidence intervals through interactive charts with zoom and export capabilities.
03
Forecast Accuracy Metrics
Compare RMSE, MAE, MAPE, and MSE across forecasting models to evaluate predictive performance and support model selection.
04
Flexible Forecast Horizons
Generate forecasts across different time horizons to support macroeconomic analysis and planning across different market environments.
05
Professional Reporting
Generate publication-ready charts and analytical outputs suitable for research presentations, portfolio reviews, and quantitative reports.
Core Expertise

Technical Toolkit

Structured Credit
ABS Consumer Loans Whole Loan Trading Loan Tape Analysis Collateral Analysis Cash Flow Modeling Relative Value Analysis Credit Performance Analysis Portfolio Surveillance
Market Platforms
Bloomberg Terminal BQuant Intex Microsoft Excel Microsoft PowerPoint
Programming & Quantitative Analytics
R R Shiny tidyverse ggplot2 highcharter Python Pandas NumPy scikit-learn SQL PostgreSQL LangChain OpenAI API
Education & Certifications

Academic Background

Education
State University of New York at Buffalo
M.S. Engineering Science (Data Science)
Jomo Kenyatta University of Agriculture and Technology
B.S. Financial Engineering
Professional Development
Business Science University
Advanced coursework in R, Python, Machine Learning, AI, and Time Series Forecasting
HarvardX / edX
Professional Certificate in Data Science
Let’s Connect

Get in Touch

I’m always happy to connect with professionals interested in structured credit, securitized products, quantitative analytics, and AI applications in financial markets. Whether you’d like to discuss the markets, exchange ideas, or learn more about my work, I’d be glad to connect.

Connect on LinkedIn ↗
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