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  • Smarter, Faster, Cheaper: The Insurance Industry’s Dream

    - by Jenna Danko
    On June 3rd, I saw the Gaylord Resort Centre in Washington D.C. become the hub of C level executives and managers of insurance carriers for the IASA 2013 Conference.  Insurance Accounting/Regulation and Technology sessions took the focus, but there were plenty of tertiary sessions for career development, which complemented the overall strong networking side of the conference.  As an exhibitor, Oracle, along with several hundred other product providers, welcomed the opportunity to display and demonstrate our solutions and we were encouraged by hustle and bustle of the exhibition floor.  The IASA organizers had pre-arranged fast track tours whereby interested conference delegates could sign up for a series of like-themed presentations from Vendors, giving them a level of 'Speed Dating' introductions to possible solutions and services.  Oracle participated in a number of these, which were very well subscribed.  Clearly, the conference had a strong business focus; however, attendees saw technology as a key enabler to get their processes done smarter, faster and cheaper.  As we navigated through the exhibition, it became clear from the inquiries that came to us that insurance carriers are gravitating to a number of focus areas: Navigating the maze of upcoming regulatory reporting changes. For US carriers with European holdings, Solvency II carries a myriad of rules and reporting requirements. Alignment across the globe of the Own Risk and Solvency Assessment (ORSA) processes brings to the fore the National Insurance of Insurance commissioners' (NAIC) recent guidance manual publication. Doing more with less and to certainly expect more from technology for less dollars. The overall cost of IT, in particular hardware, has dropped in real terms (though the appetite for more has risen: more CPU, more RAM, more storage), but software has seen less change. Clearly, customers expect either to pay less or get a lot more from their software solutions for the same buck. Doing things smarter – A recognition that with the advance of technology to stand still no longer means you are technically going backwards. Technology and, in particular technology interactions with human business processes, has undergone incredible change over the past 5 years. Consumer usage (iPhones, etc.) has been at the forefront, but now at the Enterprise level ever more effective technology exploitation is beginning to take place. That data and, in particular gleaning knowledge from data, is refining and improving business processes.  Organizations are now consuming more data than ever before, and it is set to grow exponentially for some time to come.  Amassing large volumes of data is one thing, but effectively analyzing that data is another.  It is the results of such analysis that leads to improvements both in terms of insurance product offerings and the processes to support them. Regulatory Compliance, damned if you do and damned if you don’t! Clearly, around the globe at lot is changing from a regulatory perspective and it is evident that in terms of regulatory requirements, whilst there is a greater convergence across jurisdictions bringing uniformity, there is also a lot of work to be done in the next 5 years. Just like the big data, hidden behind effective regulatory compliance there often lies golden nuggets that can give competitive advantages. From Oracle's perspective, our Rating Engine, Billing, Document Management and Insurance Analytics solutions on display served to strike up good conversations and, as is always the case at conferences, it was a great opportunity to meet and speak with existing Oracle customers that we might not have otherwise caught up with for a while. Fortunately, I was able to catch up on a few sessions at the close of the Exhibition.  The speaker quality was high and the audience asked challenging, but pertinent, questions.  During Dr. Jackie Freiberg’s keynote “Bye Bye Business as Usual,” the author discussed 8 strategies to help leaders create a culture where teams consistently deliver innovative ideas by disrupting the status quo.  The very first strategy: Get wired for innovation.  Freiberg admitted that folks in the insurance and financial services industry understand and know innovation is important, but oftentimes they are slow adopters.  Today, technology and innovation go hand in hand. In speaking to delegates during and after the conference, a high degree of satisfaction could be measured from their positive comments of speaker sessions and the exhibitors. I suspect many will be back in 2014 with Indianapolis as the conference location. Did you attend the IASA Conference in Washington D.C.?  If so, I would love to hear your comments. Andrew Collins is the Director, Solvency II of Oracle Financial Services. He can be reached at andrew.collins AT oracle.com.

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  • The Virtues and Challenges of Implementing Basel III: What Every CFO and CRO Needs To Know

    - by Jenna Danko
    The Basel Committee on Banking Supervision (BCBS) is a group tasked with providing thought-leadership to the global banking industry.  Over the years, the BCBS has released volumes of guidance in an effort to promote stability within the financial sector.  By effectively communicating best-practices, the Basel Committee has influenced financial regulations worldwide.  Basel regulations are intended to help banks: More easily absorb shocks due to various forms of financial-economic stress Improve risk management and governance Enhance regulatory reporting and transparency In June 2011, the BCBS released Basel III: A global regulatory framework for more resilient banks and banking systems.  This new set of regulations included many enhancements to previous rules and will have both short and long term impacts on the banking industry.  Some of the key features of Basel III include: A stronger capital base More stringent capital standards and higher capital requirements Introduction of capital buffers  Additional risk coverage Enhanced quantification of counterparty credit risk Credit valuation adjustments  Wrong  way risk  Asset Value Correlation Multiplier for large financial institutions Liquidity management and monitoring Introduction of leverage ratio Even more rigorous data requirements To implement these features banks need to embark on a journey replete with challenges. These can be categorized into three key areas: Data, Models and Compliance. Data Challenges Data quality - All standard dimensions of Data Quality (DQ) have to be demonstrated.  Manual approaches are now considered too cumbersome and automation has become the norm. Data lineage - Data lineage has to be documented and demonstrated.  The PPT / Excel approach to documentation is being replaced by metadata tools.  Data lineage has become dynamic due to a variety of factors, making static documentation out-dated quickly.  Data dictionaries - A strong and clean business glossary is needed with proper identification of business owners for the data.  Data integrity - A strong, scalable architecture with work flow tools helps demonstrate data integrity.  Manual touch points have to be minimized.   Data relevance/coverage - Data must be relevant to all portfolios and storage devices must allow for sufficient data retention.  Coverage of both on and off balance sheet exposures is critical.   Model Challenges Model development - Requires highly trained resources with both quantitative and subject matter expertise. Model validation - All Basel models need to be validated. This requires additional resources with skills that may not be readily available in the marketplace.  Model documentation - All models need to be adequately documented.  Creation of document templates and model development processes/procedures is key. Risk and finance integration - This integration is necessary for Basel as the Allowance for Loan and Lease Losses (ALLL) is calculated by Finance, yet Expected Loss (EL) is calculated by Risk Management – and they need to somehow be equal.  This is tricky at best from an implementation perspective.  Compliance Challenges Rules interpretation - Some Basel III requirements leave room for interpretation.  A misinterpretation of regulations can lead to delays in Basel compliance and undesired reprimands from supervisory authorities. Gap identification and remediation - Internal identification and remediation of gaps ensures smoother Basel compliance and audit processes.  However business lines are challenged by the competing priorities which arise from regulatory compliance and business as usual work.  Qualification readiness - Providing internal and external auditors with robust evidence of a thorough examination of the readiness to proceed to parallel run and Basel qualification  In light of new regulations like Basel III and local variations such as the Dodd Frank Act (DFA) and Comprehensive Capital Analysis and Review (CCAR) in the US, banks are now forced to ask themselves many difficult questions.  For example, executives must consider: How will Basel III play into their Risk Appetite? How will they create project plans for Basel III when they haven’t yet finished implementing Basel II? How will new regulations impact capital structure including profitability and capital distributions to shareholders? After all, new regulations often lead to diminished profitability as well as an assortment of implementation problems as we discussed earlier in this note.  However, by requiring banks to focus on premium growth, regulators increase the potential for long-term profitability and sustainability.  And a more stable banking system: Increases consumer confidence which in turn supports banking activity  Ensures that adequate funding is available for individuals and companies Puts regulators at ease, allowing bankers to focus on banking Stability is intended to bring long-term profitability to banks.  Therefore, it is important that every banking institution takes the steps necessary to properly manage, monitor and disclose its risks.  This can be done with the assistance and oversight of an independent regulatory authority.  A spectrum of banks exist today wherein some continue to debate and negotiate with regulators over the implementation of new requirements, while others are simply choosing to embrace them for the benefits I highlighted above. Do share with me how your institution is coping with and embracing these new regulations within your bank. Dr. Varun Agarwal is a Principal in the Banking Practice for Capgemini Financial Services.  He has over 19 years experience in areas that span from enterprise risk management, credit, market, and to country risk management; financial modeling and valuation; and international financial markets research and analyses.

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  • Big GRC: Turning Data into Actionable GRC Intelligence

    - by Jenna Danko
    While it’s no longer headline news that Governments have carried out large scale data-mining programmes aimed at terrorism detection and identifying other patterns of interest across a wide range of digital data sources, the debate over the ethics and justification over this action, will clearly continue for some time to come. What is becoming clear is that these programmes are a framework for the collation and aggregation of massive amounts of unstructured data and from this, the creation of actionable intelligence from analyses that allowed the analysts to explore and extract a variety of patterns and then direct resources. This data included audio and video chats, phone calls, photographs, e-mails, documents, internet searches, social media posts and mobile phone logs and connections. Although Governance, Risk and Compliance (GRC) professionals are not looking at the implementation of such programmes, there are many similar GRC “Big data” challenges to be faced and potential lessons to be learned from these high profile government programmes that can be applied a lot closer to home. For example, how can GRC professionals collect, manage and analyze an enormous and disparate volume of data to create and manage their own actionable intelligence covering hidden signs and patterns of criminal activity, the early or retrospective, violation of regulations/laws/corporate policies and procedures, emerging risks and weakening controls etc. Not exactly the stuff of James Bond to be sure, but it is certainly more applicable to most GRC professional’s day to day challenges. So what is Big Data and how can it benefit the GRC process? Although it often varies, the definition of Big Data largely refers to the following types of data: Traditional Enterprise Data – includes customer information from CRM systems, transactional ERP data, web store transactions, and general ledger data. Machine-Generated /Sensor Data – includes Call Detail Records (“CDR”), weblogs and trading systems data. Social Data – includes customer feedback streams, micro-blogging sites like Twitter, and social media platforms like Facebook. The McKinsey Global Institute estimates that data volume is growing 40% per year, and will grow 44x between 2009 and 2020. But while it’s often the most visible parameter, volume of data is not the only characteristic that matters. In fact, according to sources such as Forrester there are four key characteristics that define big data: Volume. Machine-generated data is produced in much larger quantities than non-traditional data. This is all the data generated by IT systems that power the enterprise. This includes live data from packaged and custom applications – for example, app servers, Web servers, databases, networks, virtual machines, telecom equipment, and much more. Velocity. Social media data streams – while not as massive as machine-generated data – produce a large influx of opinions and relationships valuable to customer relationship management as well as offering early insight into potential reputational risk issues. Even at 140 characters per tweet, the high velocity (or frequency) of Twitter data ensures large volumes (over 8 TB per day) need to be managed. Variety. Traditional data formats tend to be relatively well defined by a data schema and change slowly. In contrast, non-traditional data formats exhibit a dizzying rate of change. Without question, all GRC professionals work in a dynamic environment and as new services, new products, new business lines are added or new marketing campaigns executed for example, new data types are needed to capture the resultant information.  Value. The economic value of data varies significantly. Typically, there is good information hidden amongst a larger body of non-traditional data that GRC professionals can use to add real value to the organisation; the greater challenge is identifying what is valuable and then transforming and extracting that data for analysis and action. For example, customer service calls and emails have millions of useful data points and have long been a source of information to GRC professionals. Those calls and emails are critical in helping GRC professionals better identify hidden patterns and implement new policies that can reduce the amount of customer complaints.   Now on a scale and depth far beyond those in place today, all that unstructured call and email data can be captured, stored and analyzed to reveal the reasons for the contact, perhaps with the aggregated customer results cross referenced against what is being said about the organization or a similar peer organization on social media. The organization can then take positive actions, communicating to the market in advance of issues reaching the press, strengthening controls, adjusting risk profiles, changing policy and procedures and completely minimizing, if not eliminating, complaints and compensation for that specific reason in the future. In this one example of many similar ones, the GRC team(s) has demonstrated real and tangible business value. Big Challenges - Big Opportunities As pointed out by recent Forrester research, high performing companies (those that are growing 15% or more year-on-year compared to their peers) are taking a selective approach to investing in Big Data.  "Tomorrow's winners understand this, and they are making selective investments aimed at specific opportunities with tangible benefits where big data offers a more economical solution to meet a need." (Forrsights Strategy Spotlight: Business Intelligence and Big Data, Q4 2012) As pointed out earlier, with the ever increasing volume of regulatory demands and fines for getting it wrong, limited resource availability and out of date or inadequate GRC systems all contributing to a higher cost of compliance and/or higher risk profile than desired – a big data investment in GRC clearly falls into this category. However, to make the most of big data organizations must evolve both their business and IT procedures, processes, people and infrastructures to handle these new high-volume, high-velocity, high-variety sources of data and be able integrate them with the pre-existing company data to be analyzed. GRC big data clearly allows the organization access to and management over a huge amount of often very sensitive information that although can help create a more risk intelligent organization, also presents numerous data governance challenges, including regulatory compliance and information security. In addition to client and regulatory demands over better information security and data protection the sheer amount of information organizations deal with the need to quickly access, classify, protect and manage that information can quickly become a key issue  from a legal, as well as technical or operational standpoint. However, by making information governance processes a bigger part of everyday operations, organizations can make sure data remains readily available and protected. The Right GRC & Big Data Partnership Becomes Key  The "getting it right first time" mantra used in so many companies remains essential for any GRC team that is sponsoring, helping kick start, or even overseeing a big data project. To make a big data GRC initiative work and get the desired value, partnerships with companies, who have a long history of success in delivering successful GRC solutions as well as being at the very forefront of technology innovation, becomes key. Clearly solutions can be built in-house more cheaply than through vendor, but as has been proven time and time again, when it comes to self built solutions covering AML and Fraud for example, few have able to scale or adapt appropriately to meet the changing regulations or challenges that the GRC teams face on a daily basis. This has led to the creation of GRC silo’s that are causing so many headaches today. The solutions that stand out and should be explored are the ones that can seamlessly merge the traditional world of well-known data, analytics and visualization with the new world of seemingly innumerable data sources, utilizing Big Data technologies to generate new GRC insights right across the enterprise.Ultimately, Big Data is here to stay, and organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be the ones that are well positioned to make the most of it. A Blueprint and Roadmap Service for Big Data Big data adoption is first and foremost a business decision. As such it is essential that your partner can align your strategies, goals, and objectives with an architecture vision and roadmap to accelerate adoption of big data for your environment, as well as establish practical, effective governance that will maintain a well managed environment going forward. Key Activities: While your initiatives will clearly vary, there are some generic starting points the team and organization will need to complete: Clearly define your drivers, strategies, goals, objectives and requirements as it relates to big data Conduct a big data readiness and Information Architecture maturity assessment Develop future state big data architecture, including views across all relevant architecture domains; business, applications, information, and technology Provide initial guidance on big data candidate selection for migrations or implementation Develop a strategic roadmap and implementation plan that reflects a prioritization of initiatives based on business impact and technology dependency, and an incremental integration approach for evolving your current state to the target future state in a manner that represents the least amount of risk and impact of change on the business Provide recommendations for practical, effective Data Governance, Data Quality Management, and Information Lifecycle Management to maintain a well-managed environment Conduct an executive workshop with recommendations and next steps There is little debate that managing risk and data are the two biggest obstacles encountered by financial institutions.  Big data is here to stay and risk management certainly is not going anywhere, and ultimately financial services industry organizations that embrace its potential and outline a viable strategy, as well as understand and build a solid analytical foundation, will be best positioned to make the most of it. Matthew Long is a Financial Crime Specialist for Oracle Financial Services. He can be reached at matthew.long AT oracle.com.

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