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At Harnham, we understand the importance of a strong risk team who can develop bespoke strategies and carry out thorough checks. We make it our mission to utilize our vast data and analytics knowledge to deliver a professional and quality experience.

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RISK AND REWARD – YOUR RISK ANALYTICS TEAM

Apartment applications. Job applications. Credit card and bank applications. We’re sharing our data today like never before and with the advent of AI and other technological advances, we’re sharing at a more rapid rate. Data breaches and unethical behaviors give us pause before we jot down our most precious information but, ultimately, there’s no stemming the tide. So, who watches out for us, the customer and the company? Enter the Risk Management Team. It All Begins with Perception In May 2018, the General Data Protection Regulation (GDPR) became law across the European Union. Its goal? To place stringent requirements on how business handles customer data. Make no mistake, however, the need and the desire is not EU-specific. It is a matter of trust and security; something customers today demand, for the most part, before signing their information away to be organized, catalogued, and analyzed. Risk teams ensure your data will be used appropriately and ensure processes for future applications.  How do they do this? Risk teams need a cross-pollination of skillsets to help mitigate risk across industries. Often, risk begins in the financial sector, but it can also incorporate project management, data teams, marketing, sales, and Business Intelligence officials. And, with the advances of technology, they may also utilize Artificial Intelligence and Machine Learning to model historical data for future predictions. They must ask the right questions, ensure the right data is used for the right purpose, and validate their findings in a real-world environment. Roles of Risk Though in today’s market, everyone has a part to play, those who are focused on risk and considered part of the Risk Management Team might include the following: Chief Financial Officer (CFO) and Board Members or StakeholdersBusiness Analyst and Data Science OfficerRisk Analyst and Project ManagerStrategy and Predictive ModellerIT Marketing Together, these individuals work to challenge models, data, and decisions on behalf of customers while adhering to the company’s bottom line. Though Big Data and advanced analytics have evolved, the need to understand risks which differ in complexity, type, speed, and size remains. A few questions your Risk team might find itself asking, include: What is the impact of data and how it’s analyzed? Have we invested enough in human capital and technology, and advanced Data Analytics to focus on any potential risk including but not limited to cyber risk?Are our validations timely and appropriate? Who is responsible for decisions made by AI?Do we have the right people in place? The right tools? Are we willing and ready to challenge our data-driven and analytics-related risks?What’s our risk perspective? Do we have a good plan in place? Who will help us put one together and implement it? Ultimately, risk management in any sector, is the integration of people, processes and tools to ensure early identification and solution of risk across the enterprise. Setting the Stage or How to Get Your Risk Management Started Get buy-in from senior leadership and stakeholders as well as their commitment and dedicated participation to manage enterprise-wide risk.Make Risk Management a priority and enforce it throughout its life-cycle.Ensure technical and program management are both represented.Program management and engineering specialties should be communicated to ensure the right information is generated to help mitigate risk.Ensure risk team members, particularly those in program management, identify any concerns such as contracting, funding, costs, risks, and anything which might promote potentially dangerous ramifications if left unchecked. Even before your players are in place, you may want to consider a Risk Management Plan. Your team can help develop the parameters and implement it, but first you need to know what it is you need to watch. The CFO role in the risk team involves knowing who to pull together, what to look for, and to execute any cost-saving measures through a well-thought out plan to mitigate risk.  Four Items to Consider When Creating Your Credit Risk Team As important as technological advances have become to help mitigate risk, a business still needs human capital to analyze AI decisions and offer creative solution. So, the first two items to consider when building your team may seem unusually obvious. But, the second two, may not be so clearly necessary. These included oversight and systems-wide supply chain webs of data which must be carefully tended. TechnologyHuman Capital - Get everyone on board to ensure the program’s support; Assemble the appropriate people to assess the firm’s risks; Educate your team; Set your risk level.Supply Chain - Globalization has made companies’ supply chains more vulnerable than ever. Risk Governance - Conduct a SWOT analysis (Strengths/Weaknesses/Opportunities/Threats) to help engage your company members at every level as subtly work in broader educational efforts. Want to help the 99% have access to funds they need to live the lives they want? We may have a role for you. Take a look at our latest opportunities get in touch with one of our expert consultants to find out more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

Stephanie Brooks

Stephanie Brooks

28. March 2019

Finance 4.0 – Who Determines Credit Risk in the Fourth Industrial Age?

If you’ve applied for a credit card or loan recently, you’ll be aware of the swift response you now receive. No human can crunch the numbers and make the determination that fast, right? Although big banks are now adopting Big Data, Machine Learning, and AI into their legacy processes, startups have been disrupting the sector for a few years now. As banks and credit unions scramble to keep up, Fintech innovation is bringing together machine language, analytics, and AI to help form Big Data decisions in the industry. The forward-thinking potential of these technologies has led to some real-world uses to combat fraud, offer access to alternative data sources, and suggest real-time analysis for risk. So, Robots are Determining My Credit Risk? Well, yes and no. Often, those in the financial sector are using AI to assess Credit Risk. What once required Risk Analysts to determine manually, is now done in a matter of seconds with an early warning system developed by ING, PwC, and Google. This AI-powered system helps analysts make faster and more informed decisions about potential risk. How do they do this? Using pre-set criteria, they can gauge and analyze risk based on parameters such as whether or not a client has negative media coverage or if a share price falls below a certain percentage. If the world today is based on perception, even such items as bad reviews, negative coverage, and lower than average share prices can affect determinates. In addition, having these parameters can also help determine best practices and how businesses and individuals can be given opportunities outside the scope of big bank processes. However, as data breaches continue to mar profiles of both individuals and business, Machine Learning components offer platforms the chance to stem the tide of negativity. How Machine Learning Helps Prevent Fraud This is a simple process which requires two key measures. The first is to feed the machine not just a large amount of data, but knowing the parameters set, so the machine is fed relevant information. The second is human input which gives the machine its parameters to operate by. From there, the software will take the information, gain an understanding of the data patterns, and identify any signs of fraud. If done well, the automation process will employ solutions without sacrificing quality. Machine Learning in Determining Scorecard Models Alternative data sources offer more options not only to banks and credit unions, but also to borrowers. Using Machine Learning creates a more flexible, robust model when it comes to the type of information most useful to various borrower profiles. Having profiles prepared allows for automated scorecard updates and can generate better responsiveness and intelligence of a borrower’s risk profile. This process can be empowering for both startup and big bank tech.  The Matured State of Analytics Though humans must initially input parameters, the benefits of Machine Learning using a decision engine can dig deeper and reach farther than ever before. This type of platform can gather a variety of scenarios across the industry and can constantly analyze the information, helping inform the processes of setting credit limits, loan origination, and risk-based pricing. As an extension of a modern analytics platform, these processes fill in the gaps where other platforms may lack the data or programming required to run effectively. But, as these platforms mature, they are helping to drive innovation throughout the Fintech industry and shaking up the outdated, cumbersome processes of old for a much more streamlined efficient operation. Want to inform decisioning and work with data engineers to build validation frameworks? Are you looking to get in on the ground floor of a startup opportunity in the Fintech industry?  If so, we may have a role for you. If you’d like to learn more, check out our current vacancies or contact one of our expert consultants to learn more.  For our West Coast Team, call (415) 614 - 4999 or send an email to sanfraninfo@harnham.com.   For our Mid-West and East Coast Teams, call (212) 796 - 6070 or send an email to newyorkinfo@harnham.com. 

Stephanie Brooks

Stephanie Brooks

21. March 2019

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