RiskSpan: Data-Driven Financial Decisions

RiskSpan: Data-Driven Financial Decisions

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Bernadette Kogler, CEO, RiskSpanBernadette Kogler, CEO
The financial institutions find it cumbersome to manage large volumes of data and derive insights from them. In particular, gaining insights from unstructured data originating from various sources, demands specialized skills and algorithms. While financial companies can unlock great potentials with data analytics, they often lack effective tools and resources. RiskSpan is a services company that provides a data analytics and modeling platform to the finance industry, aiding them to make better and accurate decisions.

The company serves its clients through RiskSpan Edge platform. Backed by its consulting experience in technology solutions for the residential mortgage, structured finance, and mortgage-backed security (MBS) trading spaces, the company developed this platform to serve mid-sized, small commercial banking, and insurance sectors with loan and securities analytics. It allows clients to access hosted loan and securities data sets. Clients can upload their own data sets and run complex models that are customizable. They can also create their own reports in formats that are easily interpretable. Catering to the banking, capital markets, and insurance industries, the modular platform is a cloud-hosted system that can store data from various sources. The platform enables clients to perform predictive and historical analytics to forecast and generate results through data visualization and explanatory reports.

One of the modules in the platform provides data libraries that enable end-to-end data management, from ingesting data and data quality maintenance to data governance and business intelligence. These libraries also contain loan and securities data for clients to train models. Agency MBS is a mortgage-backed securities (MBS) loan-level application that allows clients to gain insights by analyzing pre-hosted mortgage and consumer loan data sets, which can further be used to draw references for their own data sets.

Through its platform, RiskSpan integrates middleware technology with business-specific smart contracts and machine learning algorithms to bring efficiency to the public ledger securitization market

Refined data from the libraries reduces processing times compared to the support provided by the client’s in-house IT teams. The integrated business intelligence tools in Agency MBS allow clients to visualize MBS data through graphs and charts. The clients can also create custom data tables and export charts and graphs for marketing content. Through the structured products module, securities traders, risk managers, and portfolio managers can perform structured products valuation and cash flow analyses. The portfolio whole loan analytics module allows clients to perform informed investigations to analyze whole loan deals by running historical analysis through predictive models, helping them understand asset performance and make informed investment decisions.

RiskSpan provides custom solutions for problems related to data complexities. It helps clients design models and perform analysis on financial instruments such as whole loans, mortgage-backed securities, asset-backed securities, and credit risk transfer securities. The company’s expertise spans across advanced analytics techniques that include machine learning, RStudio, and Python. RiskSpan combines its expertise with its in-depth knowledge of the finance industry data to provide valuable insights.

RiskSpan also serves as an effective risk management partner. The company is backed by a team of model governance analysts, managers, and subject matter experts who have been approving models for over 10 years. This expertise allows them to provide insights into the models that are conceptually sound.

RiskSpan is working toward making loan and deal level data more accessible and transparent to investors through blockchain technology. Through its platform, RiskSpan integrates middleware technology with business-specific smart contracts and machine learning algorithms to bring efficiency to the public ledger securitization market.