Resume Tips for Risk Analytics



There are a number of online guides about how to write a good resume, and everyone has an opinion on what works, what is the latest style as well as how many pages it should be, making this a very subjective topic.

Start by thinking about what you are aiming to do with your resume – ultimately you want to secure an interview. Now nobody secures a role from the content of their resume alone, but a poorly written resume can cost you the opportunity to even get to interview stage.

So, what is going to get you that interview?

The basics:

1) Structure

Decision makers should be able to find the information they need quickly and easily.

2) Concise communication

It is extremely important to demonstrate your ability to get your point across in a clear way.

3) Spelling/grammar

Sounds over simplistic, but this will be looked at. Remember your resume is a document that you should have taken time to produce, so small errors will be costly.

 

How does that structure look?

This may well differ and is dependent on the level of role you are applying for. You will need to put yourself in the shoes of the decision maker – what are they looking for in order to progress you to first stage interview?

If you are a recent graduate, they will be looking at your education, but if they need people with experience, then this is the most important element for them.

Starting your resume with a short statement about you will not differ whatever level of role you may be applying for. This profile shouldn’t be too informal, and should focus on highlighting the strengths and skills you possess, relevant to the role on offer.

 

How to sum up your experience:

Technical skills (SAS, SQL for example) tend to be important for roles in Credit risk, so all relevant skills and technical knowledge like these should be highlighted. However, even more important is to clearly show how the application of your technical skills, knowledge and experience had a positive impact for your current and/or previous employers. For example – If you came up with a new strategy for improving accept rates whilst reducing bad debt costs - by what percentages did this change and what was the exact impact? Include precise, not in-depth, detail to highlight your achievements.

“Reduced bad debt costs by 13% whilst increasing accept rate by 7%” is a lot more positive than “Reduced bad debt costs and increased accept rates”.

Also it is worth explaining how you achieved something? If you had an idea that was put in to practice, then go in to a little more detail. Not too much – this is just to get you an interview after all, and you need to have something to tell them when you get to meet them beyond this information, but it should be just enough to make them interested to learn more.

For example:

“I devised a refer rate strategy, coding daily lists in SAS. Once automated, refer rates fell by 15%. We saw an instant 8.3% reduction by implementing daily lists to underwriting.”

If you have experience of managing of people or a portfolio, reflect the exact detail of the team or portfolio. This will get across your ‘gravitas’ more than a general statement about management. Again, detail is the key.  For example:

Delivered circa £25mm reduction of in-year credit loss through more effective collections strategies

Primarily responsible for UK Portfolio, which peaked at over £10BN in receivables

 

Rather than:

Delivered a reduction of losses through collections strategies

Managing a UK portfolio and a team of analysts

 

How long should your resume be?


Again, everyone has an opinion on this. As a guide 2-3 pages is a standard length. This gives ample space to concisely communicate your work experience, achievements and education – whatever level of role you may be applying for.

Should you include your interests?

Personality is important in roles within Credit Risk Analytics. You are presenting to people, and dealing with stakeholders in other business teams and will need to have well developed communication and interpersonal skills. You don’t need to include too much information on your out of work interests but you need to show that you have interests other than just application strategies for credit cards. Please bear in mind though that you should not include any jovial comments – your resume should be read as a professional document.

 

And finally

Make sure you are very familiar with your resume before any interview, including any quoted figures. This document has successfully secured you the opportunity to sell yourself to a prospective employer, so know the content thoroughly. By doing so you will be well prepared and able to confidently answer questions on all aspects of your work, achievements and education.


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Putting The Pieces Together – Setting Up Your Credit Risk Team

Putting The Pieces Together – Setting Up Your Credit Risk Team

As startups, FinTech, InsTech, and other industries shake up the status quo, it’s more important than ever for the more established institutions to break out of their comfort zones. Break out, buck up, and keep up with those leading the pack in digital transformation. Personal data, privacy regulations, and password protections are just a few of things a Credit Risk team must consider when planning their Risk Management strategies. I spoke to Ewan Dunbar from our UK office about what businesses and candidates today need to know to stay on top of their game. Here’s what he had to say when I asked what the top three roles to consider in the industry were and how they worked together? Top 3 Roles in a Credit Risk Team Process Analyst - helps to identify, design, and monitor daily processes to ensure customer accounts are efficient and effectiveData Modeler – helps to segment large amounts of data into micro and macro trends using statistical analysis. This is where solid programming experience comes in such as R and Java, though SAS is still used in older organizations, it’s being used less so as new tech startups and innovators arise. Decision Science Analyst – This role sets the wider parameters of the company’s goals using quantitative measures, then drills down to determine the best possible course of action. How Do These 3 Roles Work Together? Let’s say a customer wishes to open a bank account. The initial paperwork to be filled out and filed, entered into the system, and monitored through its lifecycle would fall to the Process Analyst. Now, the customer wishes to apply for a credit card. Here, the Data Modeler is responsible for creating a scorecard model to predict, monitor, and evaluate the customer’s ability to make timely payments. The Decision Analyst is the relationship manager who has laid out the overarching goals and following facts, variables, and other data-driven insights communicates and translates the information in a clear manner. What Kind of Education Should I Have? Big Data continues to drive growth in every industry and, by 2020, experts predict an estimated 2.7 million open jobs in Big Data and Analytics. Though it’s been touted from the rooftops for the last few years, there still remains an urgent need for qualified professionals with specific skill sets to fill the gap in these industries. And they’re not easy to find.  For roles in Credit Risk, a brand name education is the name of the game. If you’re just graduating, you have a much higher chance if you come from a red brick or Ivy League background. Experience and a focus on such subjects as statistics, computer science, and mathematics are tailor-made for this industry.  Beyond education, it’s also important that companies ensure their employees have opportunities to upskill in the areas they need most. Training pays for itself as companies invest in their employees. One Last Piece of Advice  Find your niche. This is not a place for generalists. Once you’ve determined your focus and become an expert in your field, you’ll always be in high demand. If you’re looking to dig in your heels and get set up for a strong career path, we may have a role for you. Check out our latest Risk opportunities or contact one of our expert recruitment 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. 

RISK AND REWARD – YOUR RISK ANALYTICS TEAM

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. 

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

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. 

How Businesses Can Build Better Trust through Fraud Security

How Businesses Can Build Better Trust through Fraud Security

In the wake of Cambridge Analytica, Facebook data misuse, the Equifax breach, and the latest round of political finger pointing in regard to voter and election fraud, it’s no small wonder that demand for Fraud Analysts and Cybersecurity professionals is high.  If you search: “why should businesses hire fraud analysts”, you’ll see Amazon near the top, hiring for fraud security roles. If they’re taking action, should you be as well? And as the 2018 mid-terms near, Facebook is back in the news. Determined to curtail a repeat of the issues surrounding the 2016 election, the social media giant is working diligently to remove misinformation from the platform.  But with all these giant companies making fresh efforts to tackle fraud, what does this mean for you and your business? Fraud Security Begins at Home Your employees are your first line of defense when it comes to security of your business. No matter the size, it’s important to build a culture of anti-fraud policies right from the start. Below are a few essentials that all businesses should consider: Create a list of anti-fraud policies and share them with your employees, staff, and board members. Offer fraud training for both management and employees. Create a culture of reward for whistleblowers and open lines of communication such as a hotline/tip line. Though any business should be wary of prevailing scams and frauds in the marketplace, small businesses should be especially vigilant. Business News Daily offers additional tips for small businesses looking to prevent fraud right from the hiring process. The Customer Is Always Right: Balancing Fraud Risk Management with Customer Experience Experian® released their 2018 Global Fraud and Identity Report, based on results from 500 businesses and over 5,000 customers worldwide to understand what customers think of today’s security protocols. Trust was, far and away, the biggest talking point. With over 90% of consumers using smartphones and mobile devices followed by over 80% on laptops to search and buy, online security is paramount, and the new digital currency is trust. However, businesses are now having to grapple with the tension between managing fraud and maintaining a positive customer experience. Whilst they may need to lead customers to better solutions, businesses are finding that customers favor more familiar, time-tested methods like passwords. Ironically, those methods just might be compromising the experience they are advocating for by introducing an unintended nuisance and security risk; one-quarter of consumers have forgotten a username or password within the past six months.  Building a Fraud and Risk Management Team In order to protect against fraud, IT needs to play a big role in guiding your business and bringing multiple solutions together. However, we all understand that teams dedicated to technology, risk, fraud detection, and data security take time and resources to integrate.  So, to begin, here are a few ways to introduce Fraud Analysis and Risk Management into your business: Make data and fraud security guidelines part of your business plan and include them in your budget process – will the risk outweigh the reward? Involve staff, employees, management, and stakeholders at every level in the process. Be a hero in the eyes of your customer – balance detection with the customer experience. No matter where you are in your fraud detection and security hiring efforts, we may be able to help you. We specialize across roles of all levels in Data and Analytics on a global scale, with an eye for placing the right candidates in the right roles. For more information about recruiting top talent in the credit & risk sector, get in touch: Our West Coast Team can be reached at (415) 614 4999 or sanfraninfo@harnham.com. Our Mid-West and East Coast Teams can be reached at (212) 796 6070 or newyorkinfo@harnham.com.

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