FICO: A career in data analytics – understanding CX

InsurTech Digital speaks to Louise Lunn, VP of Global Analytics Delivery at FICO, as she looks back on her career in data analytics, offering key insights into the data analytics space that businesses, financial firms and insurers can digest and implement, better managing their relationships with customers. 

She also discusses her position as a woman in tech, and why now is a great time to promote the role of women in the tech industry. 

Tell us about your journey into the data industry. Is there a story there?

Bringing together predictive analytics, decision support technologies and strategy optimisation to enrich customer data and allow organisations to proactively manage their relationships with customers was my first role, which felt like a huge challenge at the time straight out of university with only theory to pull on.  

Providing complete solutions requires a thorough understanding of business problems faced by clients, which I have developed over the years.  

My curious nature is why I have stayed in the industry for so many years — I enjoy demonstrating how our products and services help address a business’s challenges.  

You’re specialised in data analytics. What is it about the sector that attracts you? 

Data science teams play a fundamental role in responding to the critical need for banking systems to make excellent decisions in an automated fashion. 

For example, banking scams have been climbing, due to the growth in real-time payments from debit accounts. 

FICO data analysts use AI and machine learning to develop analytic models that specifically focus on identifying abnormal payment transactions in real time, to help curb fraud. So we’re solving real problems faced by businesses and people.

Creating a positive experience and prioritising customer experience and personalisation is so important in the current climate. 

Analytics teams are fundamental to this, as our work can make sure relevant and non-conflicting offers, treatments and messages are sent. And the growth of data means the door is pushed even wider for those looking for a career path in this field.  

For anyone thinking about data science as a career route, the opportunities are immense. These roles are an offshoot of several traditional technical roles, including business domain expertise, mathematicians, scientists, statisticians, and computer professionals.  All these different jobs fit into the disciplines of a data scientist.  

Are more women choosing data science as a career option these days - in your experience? 

There has never been a better time for women to be part of the technology industry, and we are seeing more women choose this career path. 

Events like the United Nations International Day of Women and Girls in Science throw a spotlight on achieving full and equal access and participation for women and girls in the field. 

The UN has highlighted that over the past decades, the global community has made great strides in inspiring and engaging women and girls in science, but the work is not over yet.

For anyone thinking about data science as a career route, the opportunities are immense. These roles are an offshoot of several traditional technical roles, including business domain expertise, mathematicians, scientists, statisticians, and computer professionals. All these different jobs fit into the disciplines of a data scientist.  

Data collection is becoming increasingly important. How can the industry safeguard individual privacy while making the most of the data available? 

In areas such as financial services, for example, consumers demand data privacy. This leads to cybersecurity considerations and the gradual adoption of more sophisticated systems for digital identity management. 

Fraudsters are now compromising account data and passwords so easily and on such a scale that the security to mitigate this has had to adapt. Improving the layered approach to identity and authentication is key – having effective barriers and adaptive measures in place to remove the predictability and easily compromised elements of authentication. 

Data privacy must be seen as a fundamental principle, the protection of the integrity of an individual’s data and accessibility of that data is of the highest priority. 

What emerging trends do you think are most significant in terms of the data collection space?

I’m excited by explainable and interpretable AI, platforms that are transparent enough to allow a human expert to identify how decisions are made. 

It will create more effective teaming between humans and machines, and help people get more comfortable with AI-driven decisions. Explainable AI will break open the black box.

Currently, organisations are building teams and piloting AI, but over the coming years, this will move to operationalising AI, driving an increase in streaming data and analytics infrastructures.

What inspires you within the data space today?

Advancements in computing and AI have created huge opportunities to solve interesting problems on a global scale. For example, the expansion in the use of mathematical optimisation has helped solve some of the supply chain problems caused by the pandemic. 

In one example, Boeing’s Jeppesen digital aviation software solved a crucial nurse scheduling problem for the intensive care unit (ICU) for Karolinska University Hospital in Stockholm, Sweden’s second-largest hospital, at the start of the COVID-19 pandemic. 

Using their scheduling system which uses our optimisation solver, a Boeing team created rosters for over 300 nurses and healthcare workers during the peak period, resulting in more workable shifts for staff and better coverage for the hospital. That shows the potential of advanced analytics to be applied to new problems quickly and not only create value but save lives.

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