Chief Data Analytics Officer for FirstRand Risk, Dr Mark Nasila, joins us on this episode of the podcast series to discuss the meaning of Artificial Intelligence and its role in the financial industry.

Emerging technologies combined with social challenges across the world, has meant that every industry has had to reimagine itself.  Banking has had to resort to remote ways of working, scale up digital channels and implement flexible terms for customers in order to keep them interested in using their products and services. One of the key technologies driving this is AI, and the banking industry had invested almost $12-billion by 2021 to address these challenges head on.

Digitisation is becoming an expectation, and Mark outlines just some of the exciting use cases he has seen in the financial services space. The digital experience starts at onboarding, and one example of this is Uber which has been integrated into many financial systems to be able to onboard customers even if they are not banked. In the Risk space, machine learning algorithms are much better suited to processing a lot of information, providing the right focus to make accurate decisions as opposed to traditional ways of judgmental credit. This has been scaled in areas such as Fraud so that not too much time is spent using a blanket approach. Another area is Risk decisioning; analysts spend so much time going through customer details, so AI is enhancing the due diligence process. The future of payments is being reimagined, for example using facial recognition systems instead of cards in supermarkets that link to customer portfolios. Quantum computing in security is better at encrypting data and can therefore identify anomalies that may indicate the presence of cyber-crime. And so the list goes on.

Mark believes it’s very important for organisations to educate on the role of AI and also the implications of adopting it. There’s much talk about AI taking the place of people’s jobs, but in fact, if implemented correctly it can drive productivity and allow us as human beings to focus on human functions which we are much better suited for. Strategies are also important in articulating where AI should be used and how it can be used productively. Governance is key to ensuring the correct use of AI, safeguarding privacy and protecting the continued value of humanity.

Chief Data Analytics Officer for FirstRand Risk, Dr Mark Nasila, joins us on this episode of the podcast series to discuss the meaning of Artificial Intelligence and its role in the financial industry.
Emerging technologies combined with social challenges across the world, has meant that every industry has had to reimagine itself. &nbsp;Banking has had to resort to remote ways of working, scale up digital channels and implement flexible terms for customers in order to keep them interested in using their products and services. One of the key technologies driving this is AI, and the banking industry had invested almost $12-billion by 2021 to address these challenges head on.
Digitisation is becoming an expectation, and Mark outlines just some of the exciting use cases he has seen in the financial services space. The digital experience starts at onboarding, and one example of this is Uber which has been integrated into many financial systems to be able to onboard customers even if they are not banked. In the Risk space, machine learning algorithms are much better suited to processing a lot of information, providing the right focus to make accurate decisions as opposed to traditional ways of judgmental credit. This has been scaled in areas such as Fraud so that not too much time is spent using a blanket approach. Another area is Risk decisioning; analysts spend so much time going through customer details, so AI is enhancing the due diligence process. The future of payments is being reimagined, for example using facial recognition systems instead of cards in supermarkets that link to customer portfolios. Quantum computing in security is better at encrypting data and can therefore identify anomalies that may indicate the presence of cyber-crime. And so the list goes on.
Mark believes it&rsquo;s very important for organisations to educate on the role of AI and also the implications of adopting it. There&rsquo;s much talk about AI taking the place of people&rsquo;s jobs, but in fact, if implemented correctly it can drive productivity and allow us as human beings to focus on human functions which we are much better suited for. Strategies are also important in articulating where AI should be used and how it can be used productively. Governance is key to ensuring the correct use of AI, safeguarding privacy and protecting the continued value of humanity.

Find out how decision makers are using data analytics to turn insights into action.

RMB’s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

Find out how decision makers are using data analytics to turn insights into action.
RMB&rsquo;s Head of Data Analytics, Matthew Bernath, talks to business leaders across sectors to explore how data analytics is being used to take the guess work out of decision making.

Data Analytics with Matthew Bernath

S2 / E1 Transforming financial services with data analytics - Rand Merchant Bank (rmb.co.za)

On this episode of the podcast series, we are joined by Chris Steyn, Shoprite Technology’s Head of Data Analytics. Serving over 24 million customers in nearly 3 000 stores, with in excess of 25 000 points of sale and 100 000 articles moving through these points, data presents very exciting opportunities for the Shoprite Group and the chance to execute their strategy of becoming smarter. From a data analytics perspective, Chris believes a focus on customer-centricity and the enablement of precision retailing is essential. Customers need to have a rewarding experience when they interact — is the product available, is the quality of the highest standard, is the pricing correct and the in-store experience enjoyable? With precision retailing, it’s all about infusing more science into the decision-making process such as inventory control, wastage and shrinkage, quality control on fresh produce etc. With so many moving parts, there are many opportunities to mine different data assets that currently exist within the organisation.

In order for data to be collected and made useable, many years have been spent on standardising the data pipelines to ensure they are complete, timeous and of a high quality. This led to a data bulk architecture with different channels, exposing more data assets to more of the organisation. ShopriteX is a new digital innovation unit that aims to marry data science, technology and talent with customer-focus at its core; consolidating all the business entities with an emphasis on commerce, rewards, and personalisation.

The focus for retail is the evolution of customers from single to multi-channel and how this activity is tracked and monitored. Location analysis, the introduction of augmented reality in different forms, and the concept of customer offer engines are very important. The old systems targeted the masses, whereas now it’s all about tailoring to the individual customer needs and desires. Shoprite continues to invest more in outside intelligence, and favours an ecosystem where exploration, ideation, research and experimentation is encouraged. They have a saying – productionise your curiosity; create a workforce that is challenging every single norm that exists within the current landscape. For organisations going forward, Chris feels it’s all about investment in human talent and potential and creating an environment where people can be informed, educated and inspired.

On this episode of the podcast series, we are joined by Chris Steyn, Shoprite Technology&rsquo;s Head of Data Analytics. Serving over 24 million customers in nearly 3 000 stores, with in excess of 25&nbsp;000 points of sale and 100&nbsp;000 articles moving through these points, data presents very exciting opportunities for the Shoprite Group and the chance to execute their strategy of becoming&nbsp;smarter. From a data analytics perspective, Chris believes a focus on customer-centricity and the enablement of precision retailing is essential. Customers need to have a rewarding experience when they interact &mdash; is the product available, is the quality of the highest standard, is the pricing correct and the in-store experience enjoyable? With precision retailing, it&rsquo;s all about infusing more science into the decision-making process such as inventory control, wastage and shrinkage, quality control on fresh produce etc. With so many moving parts, there are many opportunities to mine different data assets that currently exist within the organisation.
In order for data to be collected and made useable, many years have been spent on standardising the data pipelines to ensure they are complete, timeous and of a high quality. This led to a data bulk architecture with different channels, exposing more data assets to more of the organisation. ShopriteX is a new digital innovation unit that aims to marry data science, technology and talent with customer-focus at its core; consolidating all the business entities with an emphasis on commerce, rewards, and personalisation.
The focus for retail is the evolution of customers from single to multi-channel and how this activity is tracked and monitored. Location analysis, the introduction of augmented reality in different forms, and the concept of customer offer engines are very important. The old systems targeted the masses, whereas now it&rsquo;s all about tailoring to the individual customer needs and desires. Shoprite continues to invest more in outside intelligence, and favours an ecosystem where exploration, ideation, research and experimentation is encouraged. They have a saying &ndash; productionise your curiosity; create a workforce that is challenging every single norm that exists within the current landscape. For organisations going forward, Chris feels it&rsquo;s all about investment in human talent and potential and creating an environment where people can be informed, educated and inspired.

S2 / E5 The science behind precision retailing - Rand Merchant Bank (rmb.co.za)

On today’s episode of the podcast series, we’re joined by RMB’s Head of Telecommunication, Media & Technology Advisory, Arun Varughese. There is so much happening in the payments landscape, and not many people are aware of the plumbing that exists behind contactless payments. It may happen in mere seconds, but is actually a very complex web of electronic communication both locally and internationally.

Cash is still dominant in Africa and South Africa; 60% of payments are made using physical cash, so the market for card payments and alternative fintechs is massive. The proliferation of smartphones using digital wallet services such as Apple Pay, has enabled consumers to pay for things in a much more convenient manner. Around the world there are payment options such as WeChat, Alipay, PayPal and ever-growing cryptocurrency solutions. With all this innovation and change occurring, there is a trend of moving towards financial inclusion for most of the planet, and the move away from cash is really going to be driving it.

Data is now very much at the fore; within banks there’s a lot of analysis of consumer behaviour, such as where you swiped your card, what you purchased, how often you shop etc. When you tie that up with things such as location data, even from an economic and aggregate perspective, you can see with real-time data which direction the economy is moving.

A new development on South African shores is ‘Buy Now Pay Later’ – a method of payment that’s a direct replacement for credit cards, where everyone benefits – the fintech, the consumer, and the merchant. This system allows one to borrow money on an interest-free basis over a period of time, where you can opt to pay it back in equal installments over a 4-8 week period. It’s a very real threat to traditional banking where interest is made on credit cards and fees. It’s a closed-loop system – the data is extremely valuable; fintechs can build up a much clearer picture of their customers, allowing them to target better products and services.

From an advisory perspective, Arun’s role is to consult players in the value chain on a variety of choices, particularly around capital and how to expand their businesses. As a bank, RMB hopes to grow with their clients in this increasingly active space.

On today&rsquo;s episode of the podcast series, we&rsquo;re joined by RMB&rsquo;s Head of Telecommunication, Media &amp; Technology Advisory, Arun Varughese. There is so much happening in the payments landscape, and not many people are aware of the plumbing that exists behind contactless payments. It may happen in mere seconds, but is actually a very complex web of electronic communication both locally and internationally.
Cash is still dominant in Africa and South Africa; 60% of payments are made using physical cash, so the market for card payments and alternative fintechs is massive. The proliferation of smartphones using digital wallet services such as Apple Pay, has enabled consumers to pay for things in a much more convenient manner. Around the world there are payment options such as WeChat, Alipay, PayPal and ever-growing cryptocurrency solutions. With all this innovation and change occurring, there is a trend of moving towards financial inclusion for most of the planet, and the move away from cash is really going to be driving it.
Data is now very much at the fore; within banks there&rsquo;s a lot of analysis of consumer behaviour, such as where you swiped your card, what you purchased, how often you shop etc. When you tie that up with things such as location data, even from an economic and aggregate perspective, you can see with real-time data which direction the economy is moving.
A new development on South African shores is &lsquo;Buy Now Pay Later&rsquo; &ndash; a method of payment that&rsquo;s a direct replacement for credit cards, where everyone benefits &ndash; the fintech, the consumer, and the merchant. This system allows one to borrow money on an interest-free basis over a period of time, where you can opt to pay it back in equal installments over a 4-8 week period. It&rsquo;s a very real threat to traditional banking where interest is made on credit cards and fees. It&rsquo;s a closed-loop system &ndash; the data is extremely valuable; fintechs can build up a much clearer picture of their customers, allowing them to target better products and services.
From an advisory perspective, Arun&rsquo;s role is to consult players in the value chain on a variety of choices, particularly around capital and how to expand their businesses. As a bank, RMB hopes to grow with their clients in this increasingly active space.

S2 / E6 How fintechs use data for innovation - Rand Merchant Bank (rmb.co.za)

Patrice Rassou, Ashburton Investments’ CIO joins us on this episode of the podcast series. The investment industry is very familiar with the processing and extraction of data in order to make investment decisions. A few decades back this was largely a manual process; now data is available in a digital format with quite a lot of history, allowing for spreadsheets to be built rapidly. There are also alternative sources of data available to triangulate forecasts with what industry experts are saying, thereby improving the quality of these forecasts and also the quality of valuations.

There’s a lot of raw data available, but the real issue is how easy or difficult it is to arrange and process that data in order to be able to extract signals, and how useful these are from a predictive viewpoint. To explore these signals in financial markets, Patrice believes you need to see whether there are relationships that you can observe, and are these persistent and significant enough to make money out of. The volume of data, which is into the billions, really shows that there is a vast field to mine of both structured and unstructured data which requires a lot of computing power to handle the frequency and velocity. The key focus in financial markets now is getting the signals out of this noise. The differentiating factor is making sense of the relationships, and ensuring the data will have a lasting impact that can be explored long-term.

What is alternative data? Instead of using traditional financial information, there has been a whole industry that has sprung up with a swathe of available data. Some examples are satellite imagery that gives data on mobility, traffic outside of shopping malls, ships piling up outside of ports, trucks coming in and out of factories and so forth. There are also social media indicators allowing you to pick up whether your average person is feeling more bullish or more bearish; what their type of buying habits are, what interests them in terms of financial markets chatter, etc. Alternative data used to be quite costly to collect and difficult to integrate, where increasingly there are providers who supply these alternative sources of data, sometimes to the highest bidder to avoid overcrowding.

The world of financial analysis has really evolved from being one driven by humans, to having machines and very sophisticated quantity processes able to handle and analyse huge amounts of data and deliver returns; one Patrice feels is a race between man and machine.

Patrice Rassou, Ashburton Investments&rsquo; CIO joins us on this episode of the podcast series. The investment industry is very familiar with the processing and extraction of data in order to make investment decisions. A few decades back this was largely a manual process; now data is available in a digital format with quite a lot of history, allowing for spreadsheets to be built rapidly. There are also alternative sources of data available to triangulate forecasts with what industry experts are saying, thereby improving the quality of these forecasts and also the quality of valuations.
There&rsquo;s a lot of raw data available, but the real issue is how easy or difficult it is to arrange and process that data in order to be able to extract signals, and how useful these are from a predictive viewpoint. To explore these signals in financial markets, Patrice believes you need to see whether there are relationships that you can observe, and are these persistent and significant enough to make money out of. The volume of data, which is into the billions, really shows that there is a vast field to mine of both structured and unstructured data which requires a lot of computing power to handle the frequency and velocity. The key focus in financial markets now is getting the signals out of this noise. The differentiating factor is making sense of the relationships, and ensuring the data will have a lasting impact that can be explored long-term.
What is alternative data? Instead of using traditional financial information, there has been a whole industry that has sprung up with a swathe of available data. Some examples are satellite imagery that gives data on mobility, traffic outside of shopping malls, ships piling up outside of ports, trucks coming in and out of factories and so forth. There are also social media indicators allowing you to pick up whether your average person is feeling more bullish or more bearish; what their type of buying habits are, what interests them in terms of financial markets chatter, etc. Alternative data used to be quite costly to collect and difficult to integrate, where increasingly there are providers who supply these alternative sources of data, sometimes to the highest bidder to avoid overcrowding.
The world of financial analysis has really evolved from being one driven by humans, to having machines and very sophisticated quantity processes able to handle and analyse huge amounts of data and deliver returns; one Patrice feels is a race between man and machine.

S2 / E1 Transforming financial services with data analytics - Rand Merchant Bank (rmb.co.za)

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