Asset Managers Archives

The Curious Case of Curtailments

With more than 90% of mortgages out-of-the-money from a refinancing standpoint, the MBS market has rightly focused on activities that affect discounts, including turnover and to a much lesser extent cash-out refinancings. In this analysis we examine the source of fast speeds on new issue loans and pools.

As we dig deeper on turnover, we notice a curious behavior related to curtailments that has existed for several years but gone largely ignored in recent refi-dominated environments. Curtailment activity, especially higher-than-expected curtailments on new-production mortgages, has steadily gotten stronger in more recent vintages.

For this analysis we define a curtailment as any principal payment that is larger than the contractual monthly payment but smaller than the remaining balance of the loan, which is more typically classified as payoff due to either a refinancing or house sale. In the first graph, we show curtailment speeds for new loans with note rates that were not refinanceable on a rate/term basis.

As you can see, the 2022 vintage shows a significant uptick in curtailments in the second month. Other recent vintages show lower but still significant early-month curtailments, whereas pre-2018 vintages show very little early curtailment activity.

Digging deeper, we separate the loans by purpose: purchase vs. refi. Curtailment speeds are significantly higher among purchase loans than among refis in the first six months, with a noticeable spike at months two and three.

Focusing on purchase loans, we notice that the behavior is most noticeable for non-first-time homebuyers (non-FTHB) and relatively absent with FTHBs. The 2022-vintage non-FTHB paid nearly 6 CPR in their second month of borrowing.

What drives this behavior? While it’s impossible to say for certain, we believe that homeowners purchasing new homes are using proceeds from the sale of the previous home to partially pay off their new loan, with the sale of the previous loan coming a month or so after the close of the first loan.

How pervasive is this behavior? We looked at purchase loans originated in 2022 where the borrower was not a first-time home buyer and noted that 0.5% of the loans account for nearly 75% of the total curtailment activity on a dollar basis. That means these comparatively high, early speeds (6 CPR and higher on some pools) are driven by a small number of loans, with that vast majority of loans showing no significant curtailments in the early months.

High-curtailment loans show large payments relative to their original balances, ranging from 5% to 85% of the unpaid balance with a median value of 25%. We found no pattern with regard to either geography or seller/servicer. Looking at mortgage note rates, 80% of these high-curtailment loans were at 3.5% or lower and only 10% of these borrowers had a positive refinancing incentive at all. Only 1.5% had incentives above 25bp, with a maximum incentive of just 47bp. These curtailments are clearly not explained by rate incentive.

The relatively rarity of these curtailments means that, while in aggregate non-FTHBs are paying nearly 6 CPR in the early months, actual results within pools may vary greatly. In the chart below, we show pool speeds for 2022-vintage majors/multi-lenders, plotted against the percentage of the pool’s balance associated with non-FTHB purchases. We controlled for refi incentive by looking at pools that were out of the money by 0bp to 125bp. As the percentage of non-FTHBs in a pool increases, so does early prepayment speed, albeit with noise around the trend.

We observe that a very small percentage of non-FTHB borrowers are making large curtailment payments in the first few months after closing and that these large payments translate into a short-term pop in speeds on new production at- or out-of-the-money pools. Investors looking to take advantage of this behavior on discount MBS should focus on pools with high non-FTHB borrowers.

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Agency Social Indices & Prepay Speeds

Do borrowers in “socially rich” pools respond to refinance incentives differently than other borrowers?

The decision by Fannie and Freddie to release social index disclosure data in November 2022 makes it possible for investors to direct their capital in support of first-time homebuyers, historically underserved borrowers, and people who purchase homes in traditionally underserved areas. Because socially conscious investors likely also have interest in understanding how these social pools are likely to perform, we were curious to examine and learn whether mortgage pools with higher social ratings behaved differently than pools with lower social ratings (and if a difference existed, how significant it was). To the extent that pools rich in social factors perform better (i.e., prepay more slowly) than pools generally, we expect investors to put an even higher premium on them. This in turn should result in lower rates for the borrowers whose loans contribute to pools with higher social scores.

The data is new and we are still learning things, but we are beginning to discern some differences in prepay speeds.

Definitions

First, a quick refresher on Fannie’s and Freddie’s social index terminology:

Social Criteria Share (SCS): The percentage of loans in a given pool that meet at least one of the “social” criteria. The criteria are low-income, minority, and first-time homebuyers; homes in low-income areas, minority tracts, high-needs rural areas; homes in designated disaster areas and manufactured housing. As of December 2022, 42.12 percent of loans in the average pool satisfy at least one of these criteria.
Social Density Score (SDS): A measure of how many criteria the average loan in a given pool satisfies. For simplicity, the index consolidates the criteria into three categories – those pertaining to income, those pertaining to the borrower, and those pertaining to the property. A pool’s SDS can be zero, 1, 2, or 3 depending on the number of categories within which the loan satisfies at least one criterion. The average SDS as of December 2022 is 0.62 (out of 3).

Do social index scores impact prepay speeds?

While it remains too early to answer this question with a great deal of certainty, historical performance data appears to show that pools with below-average social index scores prepay faster than more “social” bonds.

We first looked at a high-level, simplistic relationship between prepayments and Social Density Score. In Figure 1, below, pools with below-average Social Density Scores (blue line) prepay faster than both pools with above-average SDS (black line) and pools with the very highest SDS (green line) when they are incentivized by interest rates to do so. (Note that very little difference exists among the curves when borrowers are out of the money to refi.)

Fig. 1: Speeds by Prepay Incentive and Social Density Score

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We note a similar trend when it comes to Social Criteria Share (see Fig. 2, below).

Fig. 2: Speeds by Prepay Incentive and Social Criteria Share

Social Pool Performance Relative to Spec Pools

Investors pay up for mortgage pools with specified characteristics. We thought it worthwhile to compare how certain types of spec pools perform relative to socially rich pools with no other specified characteristics.

Figure 3, below, compares the performance of non-spec pools with above-average Social Criteria Share (orange line) vs. spec pools for low-FICO (blue line), high-LTV (black line) and max $250k (green line) loans.

Note that, notwithstanding a lack of any other specific characteristics that investors pay up for, the high-SCS pools exhibit a somewhat better convexity profile than the max-700 FICO and min-95 LTV pools and slightly worse convexity (in most refi incentive buckets) than max-250k pools.

Fig. 3: Speeds by Prepay Incentive and Social Criteria Share: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools

We observe a similar effect when we compare non-spec pools with an above-average Social Density Score to the same spec pools (Fig. 4, below).

Fig. 4: Speeds by Prepay Incentive and Social Density Score: Socially Rich (Non-Spec) Pools vs. Selected Spec Pools

See how social index scores affect speeds relative to other spec pools.

Temporary Buydowns are Back. What Does This Mean for Speeds?

Mortgage buydowns are having a deja-vu moment. Some folks may recall mortgages with teaser rates in the pre-crisis period. Temporary buydowns are similar in concept. Recent declines notwithstanding, mortgage rates are still higher than they have been in years. Housing remains pricey. Would-be home buyers are looking for any help they can get. While on the other hand, with an almost non-existent refi market, mortgage originators are trying to find innovative ways to keep the production machine going. Conditions are ripe for lender and/or builder concessions that will help close the deal.

Enter the humble “temporary” mortgage interest rate buydown. A HousingWire article last month addressed the growing trend. It’s hard to turn on the TV without being bombarded with ads for Rocket Mortgage’s “Inflation Buster” program. Rocket Mortgage doesn’t use the term temporary buydown in its TV spots, but that is what it is.

Buydowns, in general, refer to when a borrower pays “points” upfront to reduce the mortgage rate to a level where they can afford the monthly payment. The mortgage rate has been “bought down” from its original rate for the entire life of the mortgage by paying a lumpsum upfront. Temporary Buydowns, on the other hand, come in various shapes and sizes, but the most common ones are a “2 – 1” (a 2-percent interest rate reduction in the first year and a 1-percent reduction in year two) and a “1 – 0” (a 1-percent interest rate reduction in the first year only). In these situations, the seller, or the builder, or the lender or a combination thereof put-up money to cover the difference in interest rate payments between the original mortgage rate and the reduced mortgage rate. In the 2-1 example above, the mortgage rate is reduced by 2% for the first year and then steps up by 1% in the second year and then steps up by another 1% in the 3^rd year to reach the actual mortgage rate at origination. So, the interest portion of the monthly mortgage payments are “subsidized” for the first two years and then revert to the full monthly payment. Given the inflated rental market, these programs can make purchasing more advantageous than renting (for home seekers trying to decide between the two options). They can also make purchasing a home more affordable (temporarily, at least) for would-be buyers who can’t afford the monthly payment at the prevailing mortgage rate. It essentially buys them time to refinance into a lower rate should interest rates fall over the subsidized time frame or they may be expecting increased income (raises, business revenue) in the future which will allow them to afford the unsubsidized monthly payment.

Temporary buydowns present an interesting situation for prepayment and default modelers. Most borrowers with good credit behave similarly to refinance incentives, barring loan size and refi cost issues. While permanent buydowns tend to exhibit slower speeds when they come in the money by a small amount since the borrower needs to make a cost/benefit decision about recouping the upfront money they put down and the refi costs associated with the new loan. Their breakeven point is going to be lower by 25bps or 50bps from their existing mortgage rate. So, their response to mortgage rates dropping will be slower than borrowers with similar mortgage rates who didn’t pay points upfront. Borrowers with temporary buydowns will be very sensitive to any mortgage rate drops and will refinance at the first opportunity to lock in a lower rate before the “subsidy” expires. Hence, such mortgages are expected to prepay at higher speeds then other counterparts with similar rates. In essence, they behave like ARMs when they approach their reset dates.

When rates stay static or increase, temporary buydowns will behave like their counterparts except when they get close to the reset dates and will see faster speeds. Two factors would contribute to this phenomenon. The most obvious reason is that temporary buydown borrowers will want to refinance into the lowest rate available at the time of reset (perhaps an ARM). The other possibility is that some of these borrowers may not be able refi because of DTI issues and may default. Such borrowers may also be deemed “weaker credits” because of the subsidy that they received. This increase in defaults would elevate their speeds (increased CBRs) relative to their counterparts.

So, for the reasons mentioned above, temporary buydown mortgages are expected to be the faster one among the same mortgage rate group. In the table below we separate borrowers with the same mortgage rate into 3 groups: 1) those that got a normal mortgage at the prevailing rate and paid no points, 2) those that paid points upfront to get a permanent lower rate and 3) those who got temporary lower rates subsidized by the seller/builder/lender. Obviously, the buydowns occurred in higher rate environments but we are considering 3 borrower groups with the same mortgage rate regardless of how they got that rate. We are assuming that all 3 groups of borrowers currently have a 6% mortgage. We present the expected prepay behavior of all 3 groups in different mortgage rate environments:

*Turnover++ means faster due to defaults or at reset

Rate	Rate Shift	6% (no pts)	Buydown to 6%(borrower-paid)	Buydown to 6% (lender-paid)
7.00%	+100	Turnover	Turnover	Turnover++*
6.00%	Flat	Turnover	Turnover	Faster (at reset)
5.75%	-25	Refi	Turnover	Refi
5.00%	-100	Refi (Faster)	Refi (Fast)	Refi (Fastest)

Overall, temporary buydowns are likely to exhibit the most rate sensitivity. As their mortgage rates reset higher, they will behave like ARMs and refi into any other lower rate option (5/1 ARM) or possibly default. In the money, they will be the quickest to refi.

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HECM Loan Data, Smart Assumptions, and Cross-Sector Trade Impact Headline New Edge Platform Functionality

ARLINGTON, Va., December 8, 2022 — RiskSpan, a leading technology company and the most comprehensive source for data management and analytics for residential mortgage and structured products, has announced a flurry of new functionality on its award-winning Edge Platform.

GNMA HECM Datasets and Involuntary Prepayment Breakdown: The GNMA HECM dataset is now available to subscribers in Edge’s Historical Performance module, allowing market participants to find performance differentials within FHA reverse mortgage data. As with conventional datasets available on Edge, users slice and dice by any loan attribute to create S-curves, aging curves, time series and other decision-useful analytics.

Edge users also can now parse GNMA buyout metrics by reason, based on whether individual loans were in delinquency, loss mitigation, or foreclosure when they were removed from the security.

Smart Assumptions: Rather than relying on static assumptions to back-fill missing credit scores, DTIs, LTVs and other data on loan acquisition tapes, the Edge Platform has begun employing a smart, dynamic approach to creating more educated estimates of missing assumptions based on other loan characteristics. Users have the option of accepting these assumptions or substituting their own.

Cross-Sector Trade Impact: As a provider of loan and securities analytics, RiskSpan is making it easier to forecast the combined performance of loan and securities portfolios together in a single view. This allows traders and analysts tools to evaluate the risk and return impact of not only different loan selections or bond selections but also cross-sector reallocation.

These new enhancements all further the Edge Platform’s purpose of providing frictionless insight, knocking down barriers to efficient, clear and data-driven valuation and risk assessment.

Comprehensive details of this and other new capabilities are available by requesting a no-obligation live demo at riskspan.com.

This new functionality is the latest in a series of enhancements that is making the Edge Platform increasingly indispensable for Agency MBS traders and investors.

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 About RiskSpan, Inc. 

RiskSpan offers cloud-native SaaS analytics for on-demand market risk, credit risk, pricing and trading. With our data science experts and technologists, we are the leader in data as a service and end-to-end solutions for loan-level data management and analytics.

Our mission is to be the most trusted and comprehensive source of data and analytics for loans and structured finance investments.

Rethink loan and structured finance data. Rethink your analytics. Learn more at www.riskspan.com.

Media contact: Timothy Willis

Asset Managers Improving Yields With Resi Whole Loans

An unmistakable transformation is underway among asset managers and insurance companies with respect to whole loan investments. Whereas residential mortgage loan investing has historically been the exclusive province of commercial banks, a growing number of other institutional investors – notably life insurance companies and third-party asset managers – have shifted their attention toward this often-overlooked asset class.

Life companies and other asset managers with primarily long-term, risk-sensitive objectives are no strangers to residential mortgages. Their exposure, however, has traditionally been in the form of mortgage-backed securities, generally taking refuge in the highest-rated bonds. Investors accustomed to the AAA and AA tranches may understandably be leery of whole-loan credit exposure. Infrastructure investments necessary for managing a loan portfolio and the related credit-focused surveillance can also seem burdensome. But a new generation of tech is alleviating more of the burden than ever before and making this less familiar and sometimes misunderstood asset class increasingly accessible to a growing cadre of investors.

Maximizing Yield

Following a period of low interest rates, life companies and other investment managers are increasingly embracing residential whole-loan mortgages as they seek assets with higher returns relative to traditional fixed-income investments (see chart below). As highlighted in the chart below, residential mortgage portfolios, on a loss-adjusted basis, consistently outperform other investments, such as corporate bonds, and look increasingly attractive relative to private-label residential mortgage-backed securities as well.

Nearly one-third of the $12 trillion in U.S. residential mortgage debt outstanding is currently held in the form of loans.

And while most whole loans continue to be held in commercial bank portfolios, a growing number of third-party asset managers have entered the fray as well, often on behalf of their life insurance company clients.

Investing in loans introduces a dimension of credit risk that investors do need to understand and manage through thoughtful surveillance practices. As the chart below (generated using RiskSpan’s Edge Platform) highlights, when evaluating yields on a loss-adjusted basis, resi whole loans routinely generate yield.

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In addition to higher yields, whole loans investments offer investors other key advantages over securities. Notably:

Data Transparency

Although transparency into private label RMBS has improved dramatically since the 2008 crisis, nothing compares to the degree of loan-level detail afforded whole-loan investors. Loan investors typically have access to complete loan files and therefore complete loan-level datasets. This allows for running analytics based on virtually any borrower, property, or loan characteristic and contributes to a better risk management environment overall. The deeper analysis enabled by loan-level and property-specific information also permits investors to delve into ESG matters and better assess climate risk.

Daily Servicer Updates

Advancements in investor reporting are increasingly granting whole loan investors access to daily updates on their portfolio performance. Daily updating provides investors near real-time updates on prepayments and curtailments as well as details regarding problem loans that are seriously delinquent or in foreclosure and loss mitigation strategies. Eliminating the various “middlemen” between primary servicers and investors (many of the additional costs of securitization outlined below—master servicers, trustees, various deal and data “agents,” etc.—have the added negative effect of adding layers between security investors and the underlying loans) is one of the things that makes daily updates possible.

Lower Transaction Costs

Driven largely by a lack of trust in the system and lack of transparency into the underlying loan collateral, private-label securities investments incur a series of yield-eroding transactions costs that whole-loan investors can largely avoid. Consider the following transaction costs in a typical securitization:

Loan Data Agent costs: The concept of a loan data agent is unique to securitization. Data agents function essentially as middlemen responsible for validating the performance of other vendors (such as the Trustee). The fee for this service is avoided entirely by whole loan investors, which generally do not require an intermediary to get regularly updated loan-level data from servicers.
Securities Administrator/Custodian/Trustee costs: These roles present yet another layer of intermediary costs between the borrower/servicer and securities investors that are not incurred in whole loan investing.
Deal Agent costs: Deal agents are third party vendors typically charged with enhancing transparency in a mortgage security and ensuring that all parties’ interests are protected. The deal agent typically performs a surveillance role and charges investors ongoing annual fees plus additional fees for individual loan file reviews. These costs are not borne by whole loan investors.
Due diligence costs: While due diligence costs factor into loan and security investments alike, the additional layers of review required for agency ratings tends to drive these costs higher for securities. While individual file reviews are also required for both types of investments, purchasing loans only from trusted originators allows investors to get comfortable with reviewing a smaller sample of new loans. This can push due diligence costs on loan portfolios to much lower levels when compared to securities.
Servicing costs: Mortgage servicing costs are largely unavoidable regardless of how the asset is held. Loan investors, however, tend to have more options at their disposal. Servicing fees for securities vary from transaction to transaction with little negotiating power by the security investors. Further, securities investors incur master servicing fees which is generally not a required function for managing whole loan investments.

Emerging technology is streamlining the process of data cleansing, normalization and aggregation, greatly reducing the operational burden of these processes, particularly for whole loan investors, who can cut out many of these intermediary parties entirely.

Overcoming Operational Hurdles

Much of investor reluctance to delve into loans has historically stemmed from the operational challenges (real and perceived) associated with having to manage and make sense of the underlying mountain of loan, borrower, and property data tied to each individual loan. But forward-thinking asset managers are increasingly finding it possible to offload and outsource much of this burden to cloud-native solutions purpose built to store, manage, and provide analytics on loan-level mortgage data, such as RiskSpan’s Edge Platform supporting loan data management and analytics. RiskSpan solutions make it easy to mine available loan portfolios for profitable sub-cohorts, spot risky loans for exclusion, apply a host of credit and prepay scenario analyses, and parse static and performance data in any way imaginable.

At an increasing number of institutions, demonstrating the power of analytical tools and the feasibility of applying them to the operational and risk management challenges at hand will solve many if not most of the hurdles standing in the way of obtaining asset class approval for mortgage loans. The barriers to access are coming down, and the future is brighter than ever for this fascinating, dynamic and profitable asset class.

Top Hedge Fund Administrator: Risk Metrics & Performance Reports via Tableau and the Cloud

A leading hedge fund administrator sought a better way to provide compliance reporting and overnight risk and portfolio reporting for its clients.

Reporting at this scale requires extraordinarily flexibility in computational bandwidth.

The Solution

RiskSpan delivered computation and distribution via the cloud of all required analytics and risk metrics to all relevant parties using the flexibility and attractive visualization of a seamless Tableau integration.

Ingestion, validation, and integration of disparate data sources (rates, implied volatility data and terms and conditions from six data vendor sources)
Reporting, distribution and publishing of the client’s full range of risk metrics, including VaR, custom aggregation, scenario analyses, interest rate shocks and other stress testing — all readily viewable to every client stakeholder via the cloud using Tableau.

Northern Trust Case Study-Risk and Reporting Using Tableau

The Edge We Provided

A fully hosted, outsourced solution. The administrator’s highly dynamic reports are delivered by way of a secure, hosted environment to a large number of diverse, institutional clients.

Residential Mortgage REIT: End to End Loan Data Management and Analytics

An inflexible, locally installed risk management system with dated technology required a large IT staff to support it and was incurring high internal maintenance costs.

Absent a single solution, the use of multiple vendors for pricing and risk analytics, prepay/credit models and data storage created inefficiencies in workflow and an administrative burden to maintain.

Inconsistent data and QC across the various sources was also creating a number of data integrity issues.

The Solution

An end-to-end data and risk management solution. The REIT implemented RiskSpan’s Edge Platform, which provides value, cost and operational efficiencies.

Scalable, cloud-native technology
Increased flexibility to run analytics at loan level; additional interactive / ad-hoc analytics
Reliable, accurate data with more frequent updates

Deliverables

Consolidating from five vendors down to a single platform enabled the REIT to streamline workflows and automate processes, resulting in a 32% annual cost savings and 46% fewer resources required for maintenance.

Large Asset Manager: Implementation of Comprehensive, Fully-Managed Risk Management Reporting System

An asset manager sought to replace an inflexible risk system provided by a Wall Street dealer. The portfolio was diverse, with a sizable concentration in structured securities and mortgage assets.

The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.

The Solution

RiskSpan’s Edge Platform delivered a cost-efficient and flexible solution by bundling required data feeds, predictive models for mortgage and structured products, and infrastructure management.

The Platform manages and validates the asset manager’s third-party and portfolio data and produces scenario analytics in a secure hosted environment.

Models + Data management = End-to-end Managed Process

The Edge We Provided

”Our existing daily process for calculating, validating, and reporting on key market and credit risk metrics required significant manual work… [Edge] gets us to the answers faster, putting us in a better position to identify exposures and address potential problems.”

— Managing Director, Securitized Products

Pac Life Case Study Graphic-Comprehensive Management Reporting System

Asset Manager: Cost-Efficient and Flexible Solution

An asset management company needed to replace an inflexible risk system provided by a Wall Street dealer. The client’s portfolio was diverse, with a sizable concentration in structured securities and mortgage assets. The legacy analytics system was rigid with no flexibility to vary scenarios or critical investor and regulatory reporting.

Every portfolio manager requires reliable and accurate analytics to manage risk and improve investment decisions. They require understanding of investment positions and the impacts on risk metrics measures such as value at risk (VaR). The faster they can assess a portfolio’s total exposure and understand the key drivers, the better they can react and align activities with the overall firm risk appetite.

“The challenge was that our existing daily process for calculating, validating and reporting market and credit risk metrics required significant manual work. If we could get to the answers faster, we would be in a much better position to identify exposures and address potential problems.”

The Solution

As a fully-managed solution, RiskSpan’s Edge Platform provides the asset manager with a cost-efficient and flexible solution. The service bundles required data feeds, infrastructure management, and predictive models for mortgages and structured products. Edge manages and validates third-party data as well as client portfolio data, and produces scenario analytics in a secure hosted environment. With the combination of models, data management, and an end-to-end managed process, Edge provides the asset manager with unmatched value.

The Benefits

Portfolio risk measures on-demand
Structured product expertise
Outsourced data management
Predictive models for mortgages
Outsourced hardware management
Customized dashboards and reports

The asset manager used the Edge Platform to cut hours from daily risk-reporting processes and free several analysts to focus on their primary task: optimizing analytics and processes that support better investment decisions.

Deliverables

Analytics Software

The Edge Platform provides for the calculation of key market risk metrics for over 70 different instrument types. The service provides for a combination of on-demand or overnight batch processing. Users have online access to platform to run ad-hoc analyses, including additional scenarios or what-if analyses. The hosted platform makes the processing speed lightning fast.

Data Management Outsourced

The Edge market-risk analytics platform integrates data from six major data vendors. Our data management services support integrated data for interest-rates, implied volatility, and terms & conditions for over 70 different instrument types. The platform includes loan-level data for Agency and non-Agency mortgage-backed products. The platform integrates seamlessly with Intex subroutines to support all structured products. Further, Edge clients have access to a team of experts in mortgage and structured product – not just technical support.

Technology and Infrastructure Management

As a hosted solution, the asset manager is able to leave management of hardware to the Edge technology team. We secure and manage all required hardware, freeing up millions of dollars in hardware acquisition costs and labor costs required to manage the infrastructure.

The Curious Case of Curtailments

Agency Social Indices & Prepay Speeds

Temporary Buydowns are Back. What Does This Mean for Speeds?

HECM Loan Data, Smart Assumptions, and Cross-Sector Trade Impact Headline New Edge Platform Functionality

Asset Managers Improving Yields With Resi Whole Loans

Top Hedge Fund Administrator: Risk Metrics & Performance Reports via Tableau and the Cloud

Residential Mortgage REIT: End to End Loan Data Management and Analytics

Large Asset Manager: Implementation of Comprehensive, Fully-Managed Risk Management Reporting System

Asset Manager: Cost-Efficient and Flexible Solution

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