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Ramneet Julka, group head of data science, financial crime risk and analytics, HSBC

Ramneet Julka, group head of data science, financial crime risk and analytics, HSBC

How is your organisation using data and analytics to support the corporate vision and purpose?

 

Within compliance in a leading financial services organisation, we are driven to protect our customers, our organisation and the integrity of the financial markets we operate in. Embracing new technology and innovation is at the core of being able to deliver this purpose as we will need to be agile and responsive in a dynamic, constantly-changing environment where criminals get smarter and more determined, technology advances, customer expectations change and the regulatory environment continues to evolve. Thus, our organisation is committed to leveraging advanced analytics and cutting-edge technology to deliver actionable insight.

 

2020 was a year like no other - how did it impact on your planned activities and what unplanned ones did you have to introduce?

 

In 2020, the resilience shown by our teams has been highly impressive as they bounced back with constructive alternative plans that embraced technology and evolved new ways of working flexibly. Due to the economic impacts of 2020, the available investment reduced and we had to make tough prioritisation calls within the business, even though the business’s desire to lead with data meant that we have still been able to progress with our key innovative technology programs in compliance.

 

However, there has been an increased onus to evidence benefits and track the progress of implementation at pace. Also, proof of concepts had to be more efficiently pursued within tighter timelines and less resource, which led to creative ways of pragmatically assessing benefits for future investment. Technically, within model development, the data scientists had to account for the unusual transaction patterns and customer behaviour observed within the data.

 

Critical projects requiring global involvement had initially scheduled off-sites to pursue alignment at key milestones effectively. These sessions and alignment had to be pursued online which has taken some getting used to and learning how we can more effectively design these remotely. Team expansion across strategic locations continued effectively, albeit with alternative onboarding plans.

Looking forward to 2021, what are your expectations for data and analytics within your organisation?

 

In 2021, we intend to go live with cloud-based machine learning capabilities in the UK that should start to show tangible business returns. It sets the foundation for a global extension of these capabilities and the visible impact should help to drive continued investment into technology and analytics. In parallel, we are further tightening the governance, documentation and rigour across all our models, with an additional focus on delving into the ethical considerations and monitoring of AI & ML models. Finally, being open and curious in pursuing these analytical advancements is at the heart of the culture we are building across teams.

 

Is data for good part of your personal or business agenda for 2021? If so, what form will it take?

 

To shape better outcomes for customers this year, I have been leading the discussion on ethical considerations; educating senior executives on the ethics involved; and leading the development of new capabilities for ethical monitoring of the new AI and ML models. This is a relatively less mature area of AI and ML in the wider industry as well, and as we pioneer on machine learning models in compliance, it will be a perfect opportunity to test the ethical and model governance framework for these new models, with a view to expand that to the fuller model set.

What has been your path to power?

 

After an MBA from the leading Indian management institute IIM-Bangalore, I joined Standard Chartered Bank as a management trainee. After multiple rotations, I was intrigued how data could power and shape business decisions and that started my analytics career. When I moved to London, I joined American Express and Barclaycard in roles where I led design and delivery of risk mitigation across the customer lifecycle.

 

I then stepped into senior leadership roles with HSBC & Barclays where I have led high impact transformational programs and successfully unlocked commercial value from data, while remaining passionate about improving customer and colleague experiences.

 

I am an evangelist for the power of data science driving exponential business impact. This impact is dually powered through, firstly, actively influencing senior executives and working collaboratively with matrixed teams across large organisations, and, secondly, through technical expertise in enterprise data, data science and emerging technologies. Also, this delivery is grounded with expertise in data privacy, risk governance and controls, regulatory landscape and relations.

 

Finally, success is enabled by leading high-performing and inspired, cross-functional and multi-site large teams (Ie, analytics centres of excellence). I remain personally passionate about investing in people and building out both data scientists and bilingual data consultants.

 

What is the proudest achievement of your career to date?

 

I’ve been fortunate to have had a wide-ranging career across financial organisations in multiple geographies, leading dynamic successful multi-site analytic teams. Thus, I’m most proud of the various data science teams I have led in my career, as they have consistently delivered tangible and sizeable business value from data and analytics. These impacts have been made possible by a brilliant set of colleagues in these teams who have been passionate and driven in harnessing the power of analytics and insights into uncharted parts of the business across multiple touchpoints, while effectively partnering with senior stakeholders at the same time.

 

Tell us about a career goal or a purpose for your organisation that you are pursuing?

 

Within compliance, we are pioneering a new cloud-based system to tackle money laundering. This is a transformational project which will significantly strengthen our money laundering controls and drive increased consistency, efficiency and effectiveness of our AML processes. The tool leverages the computational power of cloud and the expertise of our data science team to build machine learning capabilities that analyse the financial activities of our 38 million customers. It generates a financial crime score based on the customer’s activity and wider network which helps to detect riskier clients. Thus, it’s a highly valuable project that efficiently and effectively protects our customers and reputation.

How closely aligned to the business are data and analytics both within your own organisation and at an industry level? What helps to bring the two closer together?

 

Across most financial services organisations, analytics tends to be embedded within the business, while data often sits with the IT function. That said, while structurally the teams are well designed, the industry is still on the journey to be truly data-driven - the business will often use data to support decisions rather than genuinely make decisions based on an unbiased view of the data. Also, data quality and consolidation across the disparate systems and business process is a persistent issue that is continually improving, but still some way off from being acceptable.

 

To help bring it all closer together, it is imperative that the teams have shared business goals powered by a clear consistent data strategy. Also, the analytic teams should have bilingual experts and data leaders who can effectively translate, influence and align the analytics strategy to the business plan and who can form effective partnerships within the business network.

 

Further, there should be a focus on driving and showcasing the tangible value that analytics delivers to the business. This will ensure sustained investments into data and technology which will accelerate continued developments in that area. In turn, this could power data-driven exponential organisational growth.

 

What is your view on how to develop a data culture in an organisation, building out data literacy and creating a data-first mindset?

 

A data culture has to start with advocacy from the top, where analytics is at the core of its purpose. Organisationally, data innovation is accelerated by establishing analytics within the business so that success is measured directly by the business benefit it drives.

 

Data ambassadors embedded across the organisation should be driving evidence-based decision-making and encouraging quick proofs of concept before investing heavily in any change. Data access along with the analytical interpretation of the data should be democratised. Data literacy is thus encouraged by showcasing the benefits driven by using data across the organisation.

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