Check out key takeaways from our discussion with risk leaders in NYC on navigating market volatility, AI disruption, and increasing system complexity.
Evalueserve is excited to sponsor the Exchange Conference 2025, reinforcing our continued commitment to the index and quantitative investment community.
Read our blog to learn what generative AI can and cannot accomplish in a SAS to Python migration.
Generative AI is revolutionizing credit risk management by boosting efficiency and decision-making. In our latest blog, we share key takeaways from our panel at RiskMinds International.
Evalueserve is thrilled to announce our recognition by Chartis Research as a Category Leader in three areas of Model Risk Management in their 2024 RiskTech report: Model Governance Solutions, Model Validation Solutions and Services (credit), and Model Validation Solutions and Services (derivatives).
Evalueserve is proud to be recognized in Chartis’ 2024 quadrant for Sell-Side Enterprise Market Risk Solutions, specifically designed for banks and broker-dealers, in their RiskTech Market Risk Solutions 2024 report.
Explore how financial institutions are transforming Model Risk Management with new tools, automation, and AI in this collaborative report by Chartis Research and Evalueserve.
Explore how we assisted a leading cross-border payment provider by independently reviewing model risk and AI frameworks against regulatory benchmarks and providing targeted remediation strategies
Explore how Evalueserve’s test demonstrates AI’s transformative impact on Enhanced Due Diligence (EDD). Discover the efficiencies and capabilities unlocked, shaping the future of risk management.
Read our blog on the latest model risk management revisions announced by OSFI, Canada’s regulatory authority.
To explore how risk teams at financial institutions are managing generative AI, we gathered with a group of risk executives in New York City. Risk leaders and AI experts led the conversation around the how risk teams are using generative AI and managing the risks of GenAI.
The EU AI Act, passed by the European Union, aims to regulate Artificial Intelligence technology and strike a balance between safety and innovation. Evalueserve offers expertise in implementing the guidelines and supporting organizations with risk management frameworks.
Read our summary of the recent European Risk Management Council meeting around the evolution of systemic risk in light of recent activity.
The failures of SVB and Signature Bank underline how crucial risk management in banking is. Discover the key lessons banks can learn from the 2023 banking crisis.
Explore why experts recommend adding ‘resilience’ to the ESG framework (ESG+R) in real estate to ensure long-term sustainability in the face of climate change.
Evalueserve’s view on regulatory technical standards for initial margin model validation under European Markets Infrastructure Regulation.
Discover the importance of chemical risk assessment and the challenges of toxicological literature searches in our guide for reliable and safe conclusions.
The heightened level of risk in today’s world, combined with increasing regulations, necessitates that financial institutions employ RiskTech.
Financial institutions face a multitude of risks linked to operations, governance, and compliance. Because of the sector’s importance to global economies
Published by Finextra
In order to curb the growing influence of index providers on global asset market, the US SEC is tightening regulations on index players. Bhashkar Upadhyay in this research piece aims to examine the role of new regulations that would govern the producers of financial indices, challenges and concerns in implementing them, and their impact on the industry in general.
On December 8, 2020, the Monetary Authority of Singapore (MAS) published the Guidelines on Environmental Risk Management for Insurers. These guidelines are aimed at improving the management of environmental risks by all insurers, in line with the expectations of the MAS.
These guidelines are aimed at improving the management of environmental risks by all banks, merchant banks, and finance companies, in line with the expectations of the MA
In June 2022, the Prudential Regulatory Authority (referred as the PRA/ The Regulators henceforth) published its Consultation Paper (CP6/22) on Model Risk Management Principles for financial institutions. With this document, the PRA has set out its expectation on financial institutions’ model risk management and has termed MRM as a risk in its own.
The 2021 Climate Risk Management Guideline was published by the Hong Kong Monetary Authority (HKMA) in July 2021.
Banking authorities strive to ensure strong risk management practices, including the detection, disclosure, and management of risks emanating from climate change. These practices are critical to boosting banks’ resilience to climate-related events and risks.
On December 8, 2020, the Monetary Authority of Singapore (MAS) published the Guidelines on Environmental Risk Management for Asset Managers. These guidelines are aimed at improving the management of environmental risks by fund management companies and real estate
The use of machine learning (ML) models by financial institutions has grown steadily in recent years given their enhanced capabilities and widespread potential application. However, building, adopting, and regulating machine learning models remains a challenge.
Increased model usage and complexity are overwhelming model risk management teams, but software can help them get ahead.
Evalueserve built a series of risk reporting microservices to help a Japanese investment bank meet regulatory requirements.
There are several model risk management tools available that automate manual processes to help MRM teams keep up with overwhelming demand.
The great recession of 2008 resulted in countless new financial regulations. Many of them are still top of mind for banks today as they continue to navigate implementation and reporting.
We are already seeing serious implications from Russia’s invasion of Ukraine, including the humanitarian crisis in Ukraine; the refugee crisis in Europe; a looming global food crisis; the risk of further military escalation […]
Model validation is key to assessing reliability and identifying errors and corrective actions, but high-quality validation is increasingly difficult to achieve.
A sound model risk management framework is imperative to avoid the costly consequences of misused or flawed models. Strong model governance is the key to achieving effective model risk management.
While models are invaluable decision-making tools for financial firms, there is significant risk associated with the incorrect use of models or model malfunction. Decisions based on flawed or misused models can have dire consequences and be extremely costly.
Automation in model risk management has been gaining traction, however, despite the benefits automation offers, banks still resist adopting it.
“With great risks comes great reward,” especially in banking. Banks are in the business of taking on financial risk to generate profit. However, the stakes are high, and the downside potential is huge.
Model malfunctioning has been an unexpected consequence of the pandemic, creating a new source of financial risk.
The recent pandemic significantly stressed the model tiering approach presenting a clear to need for a model risk management system.
The recently issued interagency statement SR 21-8 is a non-binding guidance note with very useful and practical suggestions on how banks can juggle resources between SR 11-7 and AML/BSA compliance.
A top US Bank was searching for a partner who could conduct a baseline validation as per SR 11-7 requirements.
RiskMinds event key takeaways – managing uncertainty, credit risk and systems, second generation risk models
and ESG stress testing.
Five key takeaways from Risk Americas 10th Annual Virtual Event.
While data can certainly be used to keep financial organizations informed and reduce their exposure to risk, it is important to consider what sort of data is most useful and what is the most effective way of gathering and using it. Not all data or all data management systems are equal.
Advanced Model Risk 2021 event offers eye-opening view of how MRM models enhanced through improved technology and practices.
The MRM infrastructure implemented by banks in the past few years has finally been put to test. The plane has met air, and risk teams need to adjust to any turbulence mid-air.
The traditional types of risk such as credit and market risk are about losses and are able to be quantified. It may not always be easy to mitigate risk, but the first step is to gather data and make calculations that provide a basis for decisions about whether any given business is worth doing.
Amit Inamder shares highlights from RiskMinds International 2020 conference
For many banks, the third quarter of 2020 is lining up two competing challenges: business planning for 2021, and continuing uncertainty about the duration and magnitude of COVID-19’s impact on their business.
Models are complicated things, and the principles of model governance, implementation, and use are there to make sense of them, make them explainable, and confirm that they work. Let’s take a closer look at how financial institutions currently do that and check our recommended practices for robust financial crime prevention, detection, and reporting.
Covid-19 has augmented the traditional weaknesses faced by model risk management (MRM) teams, such as error-prone test evidence collection processes and time-consuming deep dives, as well as an increased workload.
As of May 2020, four European monetary zones (the Eurozone, Denmark, Sweden, and Switzerland) along with Japan are firmly in negative interest rate territory.
Making Credit Risk Review Process Faster, Accurate and More Efficient.
Climate-related incidents are increasing, and institutions that are not adequately assessing risks will suffer unexpected financial losses. Banks’ perceived role in averting the course of climate change is also coming to the forefront of the public agenda, and any financial institution that is not proactively tackling the problem will likely be behind the competition in implementing future regulations as well as managing their portfolios.
This insights paper was prepared by the Risk & Compliance practice at Evalueserve. For a detailed analysis on how the Covid-19 crisis can impact your anti-financial crime operations and your immediate opportunities to enhance them during the lockdown and beyond.
KYC as risk management, rather than data management, remains elusive for financial services firms as well as for the high-value goods and other industries
Public and regulatory attention to the impact of COVID-19 on banks has been rightly focused on credit quality.
The Global Lockdown May Accelerate Automation And New Ways Of Working With Lower Costs.
Saying that the COVID-19 pandemic is creating havoc across each node of the global supply chain is an understatement.
In this post, we take a look at the key trends and themes in risk for 2020 – and some smart solutions too.
Check how targeted, smart automation can make a real difference to KYC, boosting efficiency and freeing up financial services firms’ resources to focus on fighting financial crime more successfully.
Discover the multiple ways that automation can make small but powerful changes to your KYC process.
A leading US investment bank was struggling with providing the right number of staff with the right language skills at the right time, in order to deal with the peaks and troughs in enhanced due diligence work.
See how by breaking down the validation process into its essential elements and standardizing the approach, you can make significant time and cost savings while also enhancing quality.
Increasing importance of data lineage and advent of machine learning indicates that traditional and new risk models will co-exist, as the MRM life cycle is optimized. But what will help minimize teething pains associated with this growth phase? Find out more in this blog.
This client was looking for deeper, more sophisticated analysis of clients’ portfolios in relation to their investment goals, plus a rigorous way to evaluate the performance of their accounts and financial advisers.
Developing, validating and monitoring multiple models is hard work. Regulators want high-quality, well-produced reports.
We set up a dedicated team with experience in credit risk model monitoring and various data analytics tools, including SAS. A senior risk specialist supported the team.
The client’s credit risk reporting and analytics team were tasked with meeting the reporting and analytical needs of credit risk managers across the WIM line of business.
We helped a US bank to proactively identify and manage bad actor risk, improve name screening for AML monitoring, and maintain financial advisor compliance.
MiFID II is revolutionizing the way investors pay for their research. Check the most recent trends emerging in the research industry and see how much buy-side can expect to pay for research, and what will they be willing to pay for.
As the song goes, ‘the times they are a-changing’ for index providers and users. In the past decade, we have seen the rise of various financial regulations, which have created more challenges for the index industry.
Index users and providers are facing greater scrutiny due to the expansion of financial regulations covering the index industry. Discover what are the options for index providers and users.
Compliance with BCBS 239 is not only mandatory for systemically important banks (SIBs), it also has tangible business benefits. Why then are so many banks struggling to meet the requirements for compliant risk data aggregation and reporting?
Risk data management is extremely important for banks, especially in times of financial crisis – and an efficient system for this task certainly has benefits.
Sector & Account Intelligence
MICI for Asset & Wealth Management
CI for Management Consulting Firms
ESG Intelligence for Asset Managers Fund Marketing & Digital Marketing Intellectual Property Management MICI for Real Estate
ESG Controversy Monitoring for Asset Managers
Patent Analysis
RPA For Risk Model Management
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