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Industry: Credit Risk Management

Machine Learning Credit Risk Models are More Accessible Than You Thought

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Machine Learning Credit Risk Models
are More Accessible Than You Thought

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Deploy Machine Learning in Your Financial Institution Rapidly

Why read the whitepaper?

This 7 page whitepaper examines the implications of machine learning in risk analytics and decisioning — how advancements in Machine Learning-as-a-service bring the technology within reach without the investment often required to hire an expert.

You will learn:

  1. How simply we conducted our own experiment to analyze demographic data for a credit decision.
  2. What analysts and influencers are expecting out of machine learning for lenders.
  3. The opportunities that the as-a-service model provides.


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Unleash the Power of Risk Analytics Within Your Salesforce Environment

ON-DEMAND WEBINAR

Unleash the Power of Risk Analytics
Within Your Salesforce Environment

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In this 30-minute, on-demand webinar you will learn how you can use Salesforce in your risk strategy. It shows you just how east it is to leverage the customer management power of Salesforce while gaining speed and agility in your decisioning workflows.

This live discussion demonstrates how you can:

  • Automate complex analytics and decisioning processes from your Salesforce environment.
  • Pair sophisticated intelligence and risk analytics with Salesforce for simply predictive cross-sell and upsell campaigns.
  • Integrate various structured and unstructured data sources with your Salesforce environment to create a powerful risk strategy ecosystem.
  • Keep a single set of integrated data across systems to avoid duplication or compliance concerns and to capitalize on real-time risk processes.


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Credit Where It’s Due – Stand-Out Financial Services Initiatives of 2020

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Credit Where It’s Due –
Stand-Out Financial Services Initiatives of 2020

Hark, what’s that sound???

No, it’s not angels singing, it’s the world breathing a collective sigh of relief that 2020 finally has one foot out of the door!

It’s been an unpredictable year. The Covid pandemic fundamentally changed the way we go about our daily lives, from where we work, how we shop, and how we interact with people, to how we manage our money. Many if not all industries have taken a hit, and financial services are no exception.

In what felt like an overnight shift, cash became dirty, bank branches were made off limits, and financial hardship became a reality for many previously stable businesses and families. As an industry that exists to serve customers with the products that best meet their needs, the ‘customer-centric’ mantra of many hit its first major test…

The result? Some failed, some passed, an elite few shone.

It’s those shining examples that I want to focus on today to bring 2020 to a close. Let’s give kudos to the financial services organizations whose efforts helped support families and businesses throughout the year. The resiliency, creativity, and adaptability of the teams at these incredible companies helped keep the world turning with human-centric digital-first banking experiences:

  • Starling Bank – Starling lent £1.4bn to over 40,000 UK businesses through the government-backed BBLS and CBILS funds. They moved quickly; getting accredited on 7th May and lending on 11th May. Their Coronavirus Support Hub has also been widely praised by business owners. At the start of the pandemic, Starling recognized the logistical challenges that self-isolating customers could face earlier than others and quickly launched their Connected Card – a second debit card that personal account customers could give to a trusted friend or family member to buy groceries and essential goods on their behalf. At a similar time, they also launched their virtual Never Home Alone program, to help staff and their families adjust to being at home, providing a support hub of physical and mental health tips and shared experiences.
  • Chime – US-based mobile bank, Chime, piloted a way for its users to receive their federal $1,200 stimulus checks instantly. They recently closed fundraising that valued the company at $14.5bn, making it the most valuable American fintech start-up serving retail customers. Chime has more than tripled it’s revenue and transaction volumes in the last year from customers pivoting to mobile banking services. A key differentiator lies with their checking accounts that give access to paychecks two days early and allow free use of an overdraft facility. This flexibility has resonated with customers throughout the pandemic, who are increasingly choosing to shift their primary banking accounts to Chime.
  • Ualá – Argentinian digital start-up bank Ualá launched three years ago. Their mission: to disrupt the LATAM banking space and address the challenge of financial inclusion in a region where only 54% of the adult population have a bank account and cash is still king. That balance is now shifting, and with demand for digital money services booming in recent months, Ualá’s financially inclusive customer onboarding has helped grow its user base of distributed cards to 2 million. Following growth in Argentina and a total $200m raise, Ualá recently announced an expansion to Mexico, staking their intention to be the next Latin American unicorn and lead a revolution that will change the way people see and understand their personal finances.
  • Admiral insurance – Back in April, at the outset of the UK national lockdown, car and van journeys ground to a halt and Admiral Insurance took the decision to rebate £25 to each customer through its Stay at Home refund policy in recognition of reduced claims—the only UK insurer to do so. This cost the firm £110m, but resulted in an increased retention rate of 81%, up from 68% last year – saving acquisition costs for over half a million customers. Large savings were experienced across the insurance sector and the firm hit the headlines in the summer when, as a thank you for their hard work throughout the pandemic, departing Chief Executive David Stevens gifted a £10m bonus windfall to Admiral’s 10,000 staff.
  • BBVA – BBVA moved quickly to offer special assistance to consumer and small business customers impacted by the ongoing Covid-19 pandemic, including ATM fee refunds, payment deferrals, extensions and waivers on existing loans and lines of credit, and many other offers. They also set up a $35m Covid-19 relief fund to boost the global response to the virus’s initial hit. The fund helped medical centers and hospitals around the globe through the purchase of medical equipment and supplies. In compliance with local health authorities, the fund has helped distribute 2,400 ventilators, 25,000 items of PPE, and nearly a million face masks to 265 hospitals across the BBVA footprint, with a portion of the fund allocated to helping older people and vulnerable families.
  • DBS Singapore – In response to the spread of Covid-19, DBS Singapore introduced a range of support measures and financial assistance to affected customers, including complimentary insurance coverage and home-loan-payment relief for employees in affected industries. SMEs were supported with a six-month property-loan deferment, temporary loan bridging, an extension of import facilities, digital account opening, and next-day, collateral-free business loans. The bank also launched health and education-related tools, such as online doctor consultation, online video-based lessons for kids, and taxi street-hail contact tracing. These services were tremendously popular and their free Covid-19 hospital cash insurance policy, for example, recorded more than 52,000 sign-ups a day at its peak.
  • Kuda – Nigeria’s digital-only bank, Kuda, is pioneering a new breed of financial institutions in the country! Nigeria has seen the use of digital banking services increase throughout 2020 with Kuda tripling the number of daily customers onboarded. This has led to a recent $10m seed fundraise, tipped as the largest of its kind in Africa. Kuda also launched a Covid-19 fund to help buy and distribute food and other essentials to people badly affected by the economic impacts of the pandemic in Lagos. Launched in April, the fund received an initial contribution of 500,000 naira from Kuda before being opened to the general public.
  • Covid Credit – 11:FS, Credit Kudos, Fronted
    It started with a single tweet on a Saturday morning back in March from Simon Taylor at 11:FS. The UK government had announced furlough support measures for full-time workers, but there appeared to be little plans in place for the 5m self-employed workforce to access these funds. A big piece of the puzzle was missing, and Simon quickly coordinated response from over 35 individuals from the fintech community, including Credit Kudos and Fronted, who together built an open banking journey, self-certification process, data capture, and ID verification – wrapped up as Covid Credit. Within 48hrs a functioning service was set up ready to present to the government and made available to the whole UK self-employed community. Truly inspiring. You can read the full story here.

The events of 2020 and the impact of the pandemic have been far-reaching, for the financial services industry it has provided the impetus needed to fast-track the transition from offline to online. The acceleration to serve and support customers in a digital way is a theme that is here to stay and set to continue. Businesses are racing to embrace digital transformation and the innovative teams behind these companies are finding incredible ways to use technology to power their human-centric, digital banking experiences.

The Provenir team and I would like to say a huge thank you to the people who’ve worked the extra hours and gone the extra mile to help keep the world turning throughout 2020.


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Creating Collections Superpowers: Using Predictive analytics to maximize payments and build brand-defining experiences

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Creating Collections Superpowers:
Using predictive analytics to maximize payments and build brand-defining experiences

How to Take Your Collections Team From Hero to Superhero.

With increasing pressure on collections strategies to handle increased volume, mitigate losses, and provide brand-building experiences, financial services organizations are looking for tools to help collections teams drive engagement and improve efficiency. In our latest ebook, we explore how predictive analytics can help your business to:

  • Predict risk earlier
  • Create more personalized treatment options
  • Power effective and efficient collections strategies

Download the ebook today to discover the 7 areas where predictive analytics can provide rapid and measurable improvements that empower your business to build brand loyalty and, in turn, lower loan loss reserves. Plus, if you’re looking to advance your collections strategies, we cover the three requirements you should look for in technology to see ROI in record time.

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Portfolio/Credit Line Management In the Age of Covid 19

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Portfolio/Credit Line Management
In the Age of Covid 19

Greetings from Provenir. My name is Brendan Deakin, Vice President of Sales at Provenir, Inc. I hope all who choose to read this blog post are faring well during this historically challenging time, and are remaining safe, healthy, and productive. This is especially true for those who are living and working in hard hit areas like New York, Michigan, Italy, Spain, and many other areas where the virus has caused every element of society to stop operating in a normal fashion. Our thanks should also go to all of the first responders, doctors, nurses, and other front-line medical staff who risk their lives to ensure we weather this storm as quickly and efficiently as possible.

The Covid-19 pandemic has caused all of us to re-evaluate how we operate across our personal and professional lives.  The ability to separate work/life activities has become an hour by hour proposition for many of us. From the simple process of ensuring our conference bridge/web-based meeting tools work when needed to balancing homeschooling and other home/dependent care demands against that of our respective occupational responsibilities, we are all being tested like never before. As I look across our business here at Provenir, I could not be prouder of our collective team in its ability to quickly and effectively transition to a virtual work environment, all while continuing to support our global financial services clients during a tumultuous time, one ripe with uncertainty. We were and are well prepared for this, and have flawlessly executed our business operations strategy against very trying circumstances.

As the Coronavirus Pandemic expands, our banking and lending clients will have a unique opportunity and role in helping consumers and businesses cope with the economic downturn associated with Covid 19 and need to be prepared to help customers weather these tough conditions. Besides protecting its own employees and service providers in their branches and offices from the physical health threat of the virus, banks need to ensure their business and consumer customers are offered payment extensions or other relief programs, but only as far as their credit policies and balance sheets will allow. Given the disparate impact this pandemic will have on small businesses and consumers, financial services organizations will need to pay particular focus on account management/credit line management and collections strategies which are tailored uniquely to each client’s circumstances, while at the same time helping shore up their reserves and balance sheet.

While there is not a “one size fit’s all” strategy for banks, lenders, and payment companies, we believe those that focus on the following key account management tenants will retain more loyal, profitable customers during this cycle while shoring up their balance sheet to take advantage of market conditions as they improve. The following capabilities should be top of mind for today’s credit risk executives:

  • Digitization:  Leverage your digital servicing capabilities to drive more consumers/small business customers to adopt internet and mobile servicing offerings, where and whenever possible. This is especially important for those clients who have been hesitant to adopt these services in the past. Gains made here will help reduce client attrition, improve customer satisfaction rates, all while opening up additional channels to engage and collect from consumers struggling under the financial burdens created by this pandemic.
  • Evaluation: As conditions change, for better or worse, it will be vitally important for lenders to evaluate new business rules and decision strategies in real-time/near real-time in order to drive better economic outcomes for the bank and their customers alike. While this is especially true during the Covid 19 pandemic, constant re-evaluation of your credit strategies should become the “new normal”. Those lenders that can execute this new reality will be well positioned during this downturn, and more importantly, as conditions improve.
  • Segmentation:  Explore each existing customer relationship by the nature and level of impact associated with Covid 19. This can help cluster customers and frame proactive Account Management strategies designed to not only aid the consumer during a trying time but limit the bank’s exposure to future delinquencies and charge-offs. Programs should be highly targeted to each account/consumer and can include short term payment extensions, new credit lines, fee waivers, collections treatments, and other strategies.

These and other thoughtful strategies, when executed through the right mix of data and technology, can help today’s risk management executives support their consumer and small business accounts through this Covid-19 crisis while limiting the threat to their firm’s balance sheet or the broader financial system as a whole.

Thanks for taking the time to read this!  Spring is upon us, and I wish you all nothing but the best for the remainder of 2020 and beyond.

Stay Safe!

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Will Social Distancing Propel Latin American Financial Institutions Towards Digitization?

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Will Social Distancing Propel Latin American
Financial Institutions Towards Digitization?

Just a few weeks into this crisis, the outlook, and vision of what was intended for the financial market in Latin America, was dramatically advanced. And based on these changes, experts argue that it is time to trust technology more and to adopt and accelerate its use in the products and services that various financial institutions market.

What are the advantages of Fintech? The answer is obvious: you can offer products and services, and acquire clients without having them on-site, that is, without having to appear at a bank or go to a certain physical location. However, only a small percentage of the market knows what a Fintech is, and these clients always fall back on the opportunities that traditional banks can offer them. The Fintech industry, in this period of COVID-19, will need to go through an era of education and drive to generate awareness and exposure among its potential users, to stop being a “nice to have” and finally become a reality. The Fintechs, without question, are here to stay.

The path, in the Latin American market, will be to digitize those “not digitized” and will possibly mark a trend for banks: to offer digital accounts with no monthly maintenance cost (as many Fintech companies do today) to unbanked clients. From this financial inclusion, these users would have easier access to funds from Coronavirus donations.

With the rise of open banking, Fintechs will focus on partnering with traditional banks or other entities to help them in the digitization process, empowering banks to move from an in-person model to a digital model. In the case of risk management, the trend will also be for Fintechs to help Latin American financial institutions on the path and process towards digitization. The Coronavirus will undoubtedly generate an increase in the use of digital technology in the Latin American market, and banking from home will be more useful than ever.

From a financial standpoint, technology will allow financial institutions to offer and market products in an agile and effective way. From a business perspective it will contribute to the “digitalization journey” and the growth of Fintech businesses and other financial institutions. But, perhaps the biggest motivator for taking the digital journey is that financial services organizations can better support clients through difficult times, like the current period of upheaval that Latin America and the rest of the world are going through. It is essential to have software focused on instant risk decisioning that can make it possible to grant any type of financing/credit in real-time, so that customers can access funds quickly.

The operationalization of risk models will also become a fundamental element in successful digitization, especially as financial services businesses expand their use of non-traditional data. These data sources feed innovative risk models that allow more advanced and accurate risk assessments to be made when authorizing payments or making credit approvals, without the support of traditional data, such as credit bureau scores.

The current Coronavirus crisis presents an opportunity to embark on the kind of innovation that the Fintech ecosystem in Latin America needs and, of course, to publicize what these companies offer. Right now, remote work and the possibility of working from home are changing the routine and the way we operate in our daily life. Digital and, therefore, technology will become a fundamental resource for our businesses, increasing both the reliance and the value technology solutions bring.

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A Microservices Architecture in Financial Services

WHITEPAPER

A Microservices Architecture
in Financial Services

Is your financial institution making the move to microservices?

e Microservices concept presents itself as the fulfillment of Service Oriented Architecture’s (SOA) loosely coupled promise – the answer to your development woes. Is it really a silver bullet, or should we tread cautiously?

This nine-page overview of microservices illuminates the architectural concept through the perspective of its:

  • Context
  • Advantages
  • Challenges

Download the whitepaper to gain a foundational understanding of the architectural trend in the financial services industry, and to pick up a stellar visual comparison of Microservices vs. Monolith from page 3.

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CRO’s Guide to Machine Learning

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CRO’s Guide to Machine Learning

Speak ‘Machine Learning’

As Chief Risk Officer, your job is not to know the ins and outs of Machine Learning. Your job is to responsibly manage risk.

However, according to McKinsey, Machine Learning “improves the accuracy of risk models by identifying complex, nonlinear patterns in large data sets” and it is one of the top trends in risk management1.  So, you should probably understand the basics.

This paper guides you through the technical foundation of Machine Learning — not why you should leverage it’s power, but how it works. You’ll gather key talking points to kickstart ML initiatives in your organization.

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Lost in translation—are risk model deployment challenges slowing you down?

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Lost in Translation—
are risk model deployment challenges slowing you down?

If they are, you’re not alone.

The recent Rexer Data Science Survey found that only 10-15% of companies “almost always” successfully deploy analytics models.

If your organization isn’t in that top 15%, you’re probably already feeling two things:

  1. Frustration caused by deployment delays
  2. Pressure from above to make it happen

The cost of delayed or failed deployment

So, before I get into the challenges preventing rapid deployment and how organizations can overcome these hurdles, let’s answer the bigger question… why should we care about model deployment rates?

There are many reasons why a business needs to be able to deploy a new risk model quickly and easily, but there are a few that really stand out in today’s digital-first world. Rapid deployment:

  1. Drives business growth—Analytics models are a key part of a risk strategy, they help drive business growth by making risk decisioning more accurate, which means more customers and lower default rates.
  2. Improves customer experience—Customers now expect instant everything, risk and analytics models help businesses make real-time decisions and gain customers in increasingly competitive markets
  3. Empowers competitive advantage—Companies that can test and deploy models quickly are able to make iterative changes to models using the most up-to-date data, making them better able to adapt to market demands.

Could you say that in Java, please?

One of the biggest reasons strategic analytics projects are often deployed late is the disconnection between the risk team and the development team.

The root of this developer-data scientist disconnection is that the two different groups literally don’t talk the same language. The modeling languages of choice for data scientists are generally Python, R (both open source languages), and the proprietary SAS. These are not usually the same languages preferred by developers, who favor Java, JavaScript, and variations of C such as C++.

So, typically data scientists create and test their analytics models—say a credit approval and verification application—using their languages. This work is then sent to the development teams, who then often spend a lot of time and costly effort recoding into their own languages so the model can be tested for security, compliance, impact on the infrastructure, and so on. Any changes that need be sent back to the data scientists for further review and approvals will kick off the same lengthy recoding processes, only in reverse.

The result? Fast time-to-market goes out the window. And if projects are deployed late enough, market conditions often will have changed so much that the reasons for deploying in the first place no longer exist, and the project is essentially dead on arrival.

Data delays

Another culprit in the model development and deployment process is the fact that data is very often located all over the organization in protected silos. This is particularly true in highly regulated industries like financial services, where security and privacy concerns meet compliance realities. Historical data may be found in one or more silos, and transactional and production data in others. Data scientists needing elements of all these data have to root around to find and gain access to it.

But that’s not all, the digitization of many types of data has led to a huge range of new data sources, many of which can be highly useful to data scientists when predicting credit risk or fraudulent activity. As each new data source emerges it needs to be integrated into the businesses decisioning solution if it’s to be utilized by analytics models.

While integrations should be simple, many organizations struggle with creating or updating data source integrations due to inflexible technology that requires extensive hardcoding. Each new data source included in a model can result in lengthy delays to model deployment as they need to be completed before the model can be fully tested and pushed to a live environment.

Say hello to your guide and translator: Platform technology

It’s fair to say that many of the delays to risk model deployment are caused by processes, not people. It’s also fair to say that the rapid advancement of technology has made it difficult to keep up with new analytics models to tackle an ever-evolving model. So, what can you do about it?

Well, what if your process problems caused by technology, like having to translate models from one language to another, or manually updating hardcoded integrations, could be solved by technology?

So, instead of your risk team creating a model in one language, then your dev team translating it into another language for your risk engine, you could opt for a model agnostic risk platform instead.

For data scientists and developers ‘talking different languages’, being model agnostic effectively removes the intermediate steps of recoding between the two different teams. Instead data scientists can upload their models directly in their native languages, which allows them to fully utilize new analytical techniques.

These types of platforms help prevent the loss of analytics models that never get deployed due to prolonged development and deployment cycles.

Technology can also be an effective solution for data integration challenges, which both fintechs and traditional financial institutions still struggle with as a result of hardcoded connections that are often built to serve a specific purpose at a specific time.

Today’s digital market requires businesses to be able to create agile technology that can be quickly updated or repurposed throughout an organization to meet many needs.

For optimum flexibility and business agility it’s essential that data integrations can be created, used, reused, and updated quickly and easily. Again, integrations have traditionally relied heavily on over-burdened dev teams for what should be simple adjustments. Instead of following these traditional integration processes businesses now have the opportunity to use technology that empowers business users to handle the integration mapping process.

This means that the risk team can be far less reliant on the dev team for ongoing adjustments as they can easily map source data into analytics models.

Gaining business agility through simplified model deployment processes

What this really comes down to is using technology to simplify business processes and empower people to do more. By using specialized software solutions that remove steps in the model deployment process and reduce the reliance on development your risk teams are able to focus on current problems and initiatives to drive business growth. They’re able to respond more quickly, make changes more easily and implement a risk strategy much more efficiently.

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Data Science in Financial Services: A Guide for the Modern Chief Risk Officer

WHITEPAPER

Data Science in Financial Services:
A Guide for the Modern Chief Risk Officer

With digitization sweeping through the financial services industry, the role of the chief risk officer is changing quickly. Why? Because new types of risks, increasing consumer demands, and growing competition make the financial landscape much more complex.

What does this mean for the CRO? It means that they need to find new ways to assess risk in this rapidly evolving market: Enter data science.

This whitepaper is the ultimate resource for any financial services leader who wants to learn more about how to successfully integrate data science solutions into their risk strategy. Download the whitepaper today to learn:

  • What data science is
  • The role it can play in reducing risk
  • How to overcome common data science challenges
  • How to get the most business value from any data science project


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