Lending: it's a risky business, with Carolyn Rohm

Always curious, always outspoken, Carolyn Rohm has a deep passion for helping analysts and analytical teams to engage with the wider business and stakeholders to deliver solutions that provide real value and bottom-line benefits. In today’s episode, we talk about her career in credit risk, about the different nuances of credit risk analytics in high-risk and low-risk environments, about credit scores and credit strategies, about analytics for modern collections, and about how to build teams with hard and soft skills.

Carolyn is at home online at https://www.carolynrohm.com/ (or just straight in and book a complimentary 30 minute chat at https://calendly.com/carolynrohm/30min?month=2024-04)

Luckily, much of Carolyn’s insight and wisdom is captured in her book - It’s a Risky Business: A field Guide to Retail Consumer Credit Analytics - which you can get from her site but also on Amazon.com and .co.uk and, I imagine, all the others.

But also feel free to find her on LinkedIn: https://www.linkedin.com/in/carolynrohm/

Scores4All is on LinkedIn, too, but their homepage is over here: https://www.scores4all.io/

I'm on LinkedIn and always open to new genuine connections - https://www.linkedin.com/in/brendanlegrange - please do reach out, and follow the show's page, too

Meanwhile, my action-adventure novels are on Amazon, some versions even for free, and my work with ConfirmU and our gamified psychometric scores is discussed at https://confirmu.com/ and on episode 24 of this show https://www.howtolendmoneytostrangers.show/episodes/episode-24

And finally, I'm also co-creating a new podcast called hAIghtened senses which will look at the intersection between human senses and technology, especially AI-powered technology. You can already start to follow it wherever you're listening to this one - there's only a trailer there at the moment, but we've recorded some of the early episodes and it's going to be a fun ride!

Keep well, Brendan

The full written transcript, with timestamps, is below:

Carolyn Rohm 0:00

A lot of people seem to think that a scorecard is going to solve all your problems. But a scorecard is just a tool that helps you make decisions. The strategy component arrives when you look at it and say, okay, so how do we treat specific accounts?

There were two things that I really wanted to achieve when I stepped out on my own: one was increasing retail consumer credit risk knowledge out there in analytical world, and then the other piece is I work with senior analysts as they begin to step into their first leadership roles. A lot of us analyst types, the soft skills, they don't necessarily come naturally to us, and I include myself in that, yet we all respond really well to those being done well, so it's something that we need to learn.

Brendan Le Grange 0:52

It was 1954 and Alan Blake had a problem. He had recently found at the South Sea Sub Aqua Club, that its members were complaining that the British winter was too cold for diving. Fair enough, I learned to scuba dive in India, did my advanced training in the Philippines, and only once left the tropics... and that was to the Med in summer. I'm no fan of cold water myself.

That said, I'm not sure Allan's solution was all that much better. The sea was too cold and so, with all the empathy of my old phys ed teacher, he got them into the swimming pool instead. I don't know, maybe they had an indoor heated pool, but I doubt it. Anyway, Alan Blake had just invented octopush, what we now call underwater hockey.

Welcome to How to Lend Money to Strangers with Brendan le Grange.

Carolyn Rohm, author, consultant, and trainer on all things consumer credit modelling, welcome to the show.

Carolyn Rohm 2:02

Thank you. lovely to be here.

Brendan Le Grange 2:04

It's lovely to have you. Carolyn, you're passionate about bringing the gold standard of consumer credit risk assessment to everyone. But when I saw that we were both UCT alumni, I took a closer look at your LinkedIn profile and realised that, shock and horror, credit risk might not have been your first love. So before we talk to the work that you're doing at the moment, talk to me about oceanography, psychology and how you went from that to retail credit risk.

Carolyn Rohm 2:33

So I went to university like so many people do - a lot of people, they know exactly what they're going to do, and I envy those people, but I am not one of them - I had no clue what I wanted to do.

So I did my undergrad is this entire mishmash of subjects that I loved. I've got some physics in there, some astrophysics in there, some English, some stats, some geology. And I ended up doing a double major in psychology and oceanography. Oceanography is all about ocean and atmosphere and physics. And I loved it. It was amazing. It was really interesting. And I did psychology because I also found that quite interesting, mostly because I've been told I'm really difficult my whole life. And I don't think I'm that difficult. So I wanted to understand more about that.

So yeah, that's that's kind of my undergrad, and then I did more oceanography, then I realised that perhaps I didn't want to work in science. And I did a two year stint at bat, where we manufactured cigarettes back in the bad old days. And then I landed a role as a credit risk analyst. No one has found a clear link between those things. But mostly it was stuff that was interesting. So that's what I did.

Brendan Le Grange 3:41

Yeah, well, I'm glad you did because, you know, one of the things I regret now is that I studied commerce for my undergrad degree and then commerce for my masters, I worked in a bank and it's all very straight down the line. But it's not one of those fields where you need to specialise like a doctor or an engineer. So you may as well you may as well study and become more more educated and more interesting.

But Carolyn, your leaps from one thing to another didn't end there. You then moved from the southernmost tip of Africa and somehow managed to get even further south, continuing your career in New Zealand. My gut feel is that is quite a dramatically different risk landscape than South Africa. So if we continue this sort of story of your early career, what did you learn and experience differently when you got into New Zealand and you saw the lending landscape there, compared to watch it started to get to know in South Africa?

Carolyn Rohm 4:34

That's an awesome question. So in South Africa, I primarily worked with the retailers, and they did a combination of credit cards and instal cards and personal loans to their customers. And in South Africa, we had a lot of data are available to us. So within the credit bureau, we could see positive and negative information. And if we really wanted to, we could ask the bureau to send us a whole bunch of batch characteristics, which really goes down to the number of active trade lines, you have the number of open trade lines you have, or the percentage payments, which means that we could actually do some fairly advanced credit risk analytics.

When I arrived in New Zealand, and I got my first role in a bank, I was stunned. And I honestly mean stunned to discover that from a credit bureau point of view, they only had negative information.

And what that means is they could see whether someone had made an inquiry for credit. And they could see whether someone had gone so delinquent that they had a judgement or a default against them. But that was kind of it. I mean, you could see how much the default was worth. But what you couldn't see was whether people were actually making their payments on the credit lines that they had, in fact, you couldn't even see how many credit lines someone had, nevermind what their total exposure was in terms of balance and things like that.

And I remember, I think this was back in 2009, sort of just just post GFC, just sitting there going hard, Does anyone do their job, because all the information I was so used to seeing just wasn't available. From a bureau point of view, that was probably the most dramatic change that I saw in terms of what was available from data point of view. From a risk point of view, I remember I was in a bank, and I had come out of retail in South Africa. And I was working with the collection managers and teams, and they were hugely concerned about what they consider to be their very high levels of arrears. And they were working incredibly hard. And they were doing everything that they could, and the more senior levels of the bank work looking at the levels of arrears and being really concerned and giving them a bit of a hard word. And being myself and saying what I thought without necessarily thinking it through I just said I know people in South Africa who would give their firstborn for arrears rates that look this good. And everyone thought I had lost the plot.

So yes, they're very different environments,

Brendan Le Grange 6:56

The same sort of time I was in Scandinavia and Northern Europe, and they were transitioning there from negative to comprehensive credit bureaus. And, you know, what people often didn't realise is it was the low risk the good customers who benefit by comprehensive because if you can only see a negative data, you're either bad or you're just everybody else. There's no way to find that new ones, you know, have you paid all of your one loans? Or have you paid all of your 300 loans over 20 years in different ways? Yeah, it can give us far more certainty in our modelling.

And as soon as you go comprehensive, it really just frees it up for consumers who've been paying well to get the services and rewards from that, that before we talk too long on credit bureaus and and risk differentials between South Africa and I'd argue many markets around the world where they wouldn't quite stomach the the delinquency rates that are normal there. About two years ago, I stepped out of the corporate world. And I cited the stress of working through COVID lockdowns as one of the factors involved.

But you guys in New Zealand didn't really seem to have too much of an issue with COVID. So maybe it's just coincidental, but roughly the same time they are in 2021. You also strike out on your own, not just as a credit risk trainer in the traditional sense, but as a coach and a mentor to what does that entail? What are you helping other credit analysts to do?

Carolyn Rohm 8:18

Just a little piece on COVID? We went into lockdown. And in Auckland, we went into horendus lockdown, but from a COVID impacting life yet, yes, we had it much better.

But anyway, yes, you're right, I did, I left proper corporate world. And then I worked with a much smaller organisation, which was awesome. And then I decided to go to Lowe's. And one of my key reasons for doing that and going into training and mentoring is the way I see it. There are two key gaps in the market gaps and credit risk analytics.

The first one is the fact that we learn credit risk analytics from our line managers. So if you have a line manager who has worked in a large organisation where they've seen all the different aspects from origination through to collections, a little bit of marketing, some recoveries, account management, limit increases, etc, you're likely to learn a lot. If however, you've been quite limited and where you've been exposed, you don't get to learn so much. And there are some organisations where people are inventing the wheel as they go.

So they don't necessarily have a credit risk specialist at the organisation. For example, something as simple as decline overwrites there are a lot of people who don't do their calculation, right. And this holds true across the front end. So originations, certainly around connections, so transition matrices, what they are, why they're important, how to build them, how to interpret them, and you can get hold of consultants, and therefore a pretty penny will come along and share their knowledge. But for the most part, that's how we learn.

And then the other piece of the training that I do is I work with senior analysts as they begin to step into their first leadership roles. Because the other thing that is hugely bought by I found very relevant to my world was that when you start leading, it's as if someone goes here, the keys to the car, if you go, you mean you want driving lessons to beat after school drive.

And a lot of us analysts types are super introverted and really fact oriented. And we like our processes. And we don't necessarily do the soft skills particularly well. Yet we all respond really well to those being done well, but they don't necessarily come naturally to us. And I include myself in that. But it's something that we need to learn and be aware of how do we communicate with people up the chain? How do we take analytics and when someone says, but I don't understand. As an analyst, our tendency is to double down on the detail. And when you know what you're looking for, you can literally see people lose the will to live because they don't understand and that isn't helping, and they're not number oriented. So just make it stop.

Brendan Le Grange 11:00

And of course, it's worth saying that because we live in a beautifully connected world these days, even though New Zealand is sometimes forgotten off of world maps, this is available to people around the world, the mentoring, the coaching, even the training can be delivered online, too.

And it's it's such a important aspect to any lender, while the knowledgeable technical people they've got in their credit department are like called, but you always have this difficult transition of you've got somebody starts out day one, they start to learn the tools, they've got to education, maybe in statistics, and they become better and better and more knowledgeable about the tools about how you build the models becoming more technically proficient, and then somebody leaves or whatever happens. So we now need a team manager, let's take our best analyst, you know, our most senior analyst and promote them.

Now it's great to promote from within, but you can't just take somebody who's going up a technical path and say, manage the team, they may hate their new job, they may feel like they're missing the old technical things that we're good at, and now they're out of their depth.

And you can really cause huge problems in a very important part of the business. If you don't do that, even though everybody's highly skilled and talented and wanting the best. So yeah, I think that everybody listening should now go over to www.carolynrohm.com and have a look, I will come back that in a minute. That won't disrupt us too much. But vitally important, I think, for any credit team to be thinking through the work, you're doing even more important now than ever, because this I think probably people say all the time, but it feels like we're in a very changeable moment.

So there's a lot of things we could discuss, and maybe we'll have you on another day and discuss some of them. But just to start, let's focus on what I think for many people at the moment is the most urgent one collections. So firstly, what changes are you seeing in the way that lenders are thinking about the collection strategies to collections, data processes? And how do we think of modelling in this area? How do we think about our approaches, perhaps differently,

Carolyn Rohm 13:09

Collections gets a lot of love when the economy goes slightly sideways. It does not get nearly so much love when everything is hunky dory and growing at a rate of knots.

It's such a big field. So before we even start talking about modelling, I'm going to talk about actual collection strategy design a little bit. A lot of people seem to think that a scorecard is going to solve all your problems. A scorecard to be absolutely clear is not a strategy. A scorecard is a tool that helps you make decisions.

The strategy component arrives when you look at okay, so how do we treat specific accounts, for example, people who are already in hardship, you want to pull out all of your accounts, I don't know how to contact this person, put them in a separate queue so that you can work them as someone who is under some sort of hardship or under some sort of administration, they don't go into your collections queues, those are the sorts of things that start going into strategies. And then when we start talking about strategy, we've also got to look at how we segmenting our population. Are we just grouping it based on how delinquent they are? You're one cycle delinquent there for you go into this pile and you two cycles delinquent, you go into that pile, we're going to send you off to external collection agencies, etc. So that we can treat like risk people similarly. So if someone is actually generally a really good risk, they mostly pay on time and they've just dropped into arrears, you probably want to be a little bit more customer service oriented, rather than bringing out the big guns.

Conversely, if someone is recidivist and they've been on your books for a long time, what do you need to do to try and bring them out of that and bring them back into, you know, being a good customer and paying on time? This is particularly important when we talk about loans because loan instalments tend to be quite chunky and Once you've missed that first payment, it becomes increasingly difficult to bring yourself back up to date.

So what we're seeing, certainly in this part of the world, from a collections point of view, is that arrears are now back at pre COVID levels. And in some instances, they're actually higher. And there are a lot of factors playing into this interest rates are higher cost of living is higher food I was reading this morning is something like up 18% or 20%, or something terrifying, which if you're at the higher end of the economic scale, you can absorb some of those costs. But for people who are at the lower end, there is no more room to absorb these kinds of costs, then, of course, because everyone's a little bit concerned about the cost of living, there tends to be this, we're going to shut down the front end.

And there's value in that.

But that has an impact on what happens at the back end. So what we're seeing is a lot of people with increased arrears trying to work out how to minimise those arrears without losing customers and without having to write everything off. So yeah, there's a lot of focus on how do we do things differently.

And of course, t legislation is getting more and more to the point where, in the bad old days, you could, well, not quite yell and swear, but you know, they would harass you basically. Whereas now, there's a much better sense of actually, these are still customers, and we still need to treat them like human beings.

Brendan Le Grange 16:21

You spoke about strategy, then I think if there's a silver lining coming out of the COVID world, it's hopefully that a lot of eyes, they've never used to look at collections turned their eyes towards those sort of strategies, as all customers somewhat randomly depending on what industries they were in, suddenly needing to take payment holidays, or make some plans. And they couldn't do it by going into the branch and your staff often couldn't be in the branch to take their phone call.

So there was a big push with pressure from many different directions to automate and make more customer friendly, the early stages of collections and you have spoken to some people, I think, I've been given better terms and collections for the stage of life, but I've forgotten them now. But there's early stage collections where it is customer management, where it is saying, okay, you've got some struggles, let's work with you.

And that requires a lot more nuance when it comes to things like modelling because in the old days, you could get away with just dates, see how late they are and apply some very broad strategies. Now you really do want to understand early on what sort of collections is this person and so this person, just a lazy payer, so this person may be having a short term issue with their job? Isn't a slower creep of affordability that they they need to make some adjustments? Or is it somebody who just doesn't want to pay me and needs the old fashioned yelling from the from a scary sounding person? So we grow up building models that say, you know, the target definitions, delinquency, we look for a few bad customers, we look for histories of delinquency, and we build a nice scorecard.

But when we flip it around, and we say, Okay, now somebody's rolling into collections, what do we do differently there as a model? I'm sure this is happening in banks and lenders around the world at the moment, people being pulled off from the team that used to build applications scores, and saying, we need one of these four collections. What do they maybe think about differently in that capacity?

Carolyn Rohm 18:24

The principles remain the same.

So you still need to work out what does good behaviour look like, versus bad behaviour. And when I say bad behaviour, I'm always so cognizant of in the banking world, we can be so super judgy with her language, you are bad because you didn't make a payment.

Well, actually, no, that's not strictly true. But it certainly can come across like that. But at the end of the day, we have to say zeros and ones, you're either in the group that we want more of or new group, we want less of, in the case of applications, what you're doing is you're saying, we're going to give these people credit, and we're not going to give these other people credit. In the world of arrears management, you can't just say, well, we're going to take all the credit away, because that doesn't suddenly give you the money back, you can take away their ability to spend, but they still have a balance outstanding, so you can't just disregard them. Not if you want a decent bottom line.

So from a modelling point of view, we need to look at how do we define the kind of behaviour that we want if someone makes their payment? Is that good behaviour? Or if someone misses that payment? Is that poor behaviour? And if they miss their payment? Are you looking at how many payments over a specific number of months? Or are you looking at right now right now? I'm only looking at the very short term, are you making this payment? And you can get quite nuanced depending on what kind of data you have available. So we talk about as a collector, you phoned someone up, you've spoken to them, they said, Oh, yes, actually, I will make a $50 payment every week for the next six weeks and that will catch me up and everyone's happy in the event that they failed to come keep that promise? Is that what you're modelling? Or are you modelling something else.

So the key pieces of modelling, I don't think particularly change your variables, the features that you're using, they certainly can change, you need to be clear on what you're defining as your observation point as an application scoring, really, really important that you don't take into account any events that occurred after that point. Typically, you would use the last six to 12 months worth of information on their customer, to work out the likelihood of making payments.

And then once you've got your model, the key thing is, how do you implement it? How do you work with the collections operations team, because it's all good and well having this beautiful strategy, but if ops doesn't have the faintest idea what you're trying to achieve, they're not going to do what you'd like them to do, especially in collections, it's about so much more than just the model. It's also about taking the operations team on journey, which has less to do with modelling and much more to do with change management.

Brendan Le Grange 21:04

Yeah, but it's it's change management driven by the sort of person that you're talking about mentoring upfront, because you need somebody to understand the impact. And if this is an early implementation of the sort of models, you really can be talking about big significant amounts of impact you can make, even in the old fashioned bank first mentality, you can save a lot of money in terms of write offs in terms of operational costs with ease.

And of course, now over the last decades, we've come a little bit more to appreciate the human side too. And we can see the huge impact.

And I think it's a time to look at how do we help consumers not end up in collections too late. And it's something I've already come to understand after I was working in collections, unfortunately. But one of the downsides of this is not backed up by science, just my belief, but from the old mentality of collections as a scary process, that you really didn't want to get into. Scary because someone's going to shout at you, scary because legal looking letters have been sent to you scary because, you know, it's just uncomfortable.

People try to avoid it for as long as possible, which was good for the bank. But I mean, by the time we knew there was a problem, they'd been trying to solve the problem themselves for a few months, and by definition had failed.

And what I think is really important now from a strategy side is how do we get that consumer right up front, that they know collections isn't scary that people can say early on, I'm feeling under pressure, I can see my bank account struggling, maybe I'm working less, maybe something's happened. Or maybe it's just the cost of living, I'm trying to adjust, see if I can get a one month payment holiday or if I can extend my loan by a little bit and reduce my payments from early on enough that we can save people we can help them.

And yeah, apply some of these, these new machine learning tools that are out there. There's new big data, we've got access to promise to pays and things were really hard not so long ago to capture and to model. But now those tools exist. So it's an exciting time, I think, for collections to move out of the sort of depth and the back room of the office in the operational centre into a part of that data flow. That's become so important to the banks. Honestly,

Carolyn Rohm 23:25

It's just the right thing to do. I mean, they're your customers ultimately. And if you can help someone, before it's all gone completely sideways, you've actually got yourself a customer for life. And the cost of aquiring new customers is significantly lower, we all know that.

I mean, this is this is not a newsflash, new customer acquisition is an expensive hobby.

Brendan Le Grange 23:46

And then you've still got to find the customer that uses the product. And you know, that builds a balance plus the moment where we can create the biggest customer loyalty and the biggest brand experience uptick is when something's gone wrong, and you try and get ahold of somebody to fix it. If they fix it, that's actually you end up liking that company more than before the problem happens.

But of course, if they don't, it drops off the cliff. But it really is the perfect moment to find those customers who want to work with you who will make a promise to pay, who will come in early these days on the app or something and ask for support to make those payments. Really self identifying as an ideal customer.

And if a lender is not investing in the space now if they just hoping that another few desks of outbound callers will help them get through the queues that are growing, they really missing out on on a lot of value.

Carolyn Rohm 24:40

Yeah, I think a lot of organisations would benefit from putting sort of like a customer service marketing, you know, that kind of spirit of like, we can help them and we can change the story. I guess it's at the bottom line, but it's also in their customer journey. So the there's the hard dollars, but there's also the soft squishy, we've got a customer who likes us, we did the right thing.

Brendan Le Grange 25:04

A customer who talks to us who likes us, who spends on our product. And in a world where there's more and more competition, yeah, once you've got them, you really do want to keep them. And so, Carolyn, if people are listening and are panicking and saying I need to get some scorecards built, but I don't have the human capital to pull them in house, you can help them there as well, because you're also behind Scores4All.

Carolyn Rohm 25:27

So I've been working in credit risk, as we pointed out for a very long time. And I've noticed over the years, there are a couple of consistent trends, whether we talking application scoring, behaviour, scoring, collection, scoring, doesn't matter what kind of scoring some of the larger organisations, many of the large organisations have all the bells and whistles, tools and resources. And that's awesome.

However, there are an extraordinary number of organisations that don't have this. And they also don't have the capital reserves floating around to just go out and buy some of these tools. And it's easier said than done to just quickly build a data science team. So we took all of that, and we decided to create scores for all you have data, and you want a scorecard, share that data with us, we'll run it through our engines and our proprietary models.

And we will return a score to you as an organisation, you get a score, without having to worry about the modelling, you don't have to do any of that, that stuff, you don't have to do the governance of scoring.

So you don't need to do all the reporting, we do that. And we provide that for you so that you can see how your scores work. We also train models over time, so that we've always got the most recent data going into those models delivering the best models for you. And when it comes to things like behaviour or collection scoring, most importantly, we have what we call the scoring engine. So a number of variables aren't just how delinquent are you this month, they are a function of your payments and your balances and how you've paid over a number of months. And we calculate those variables based on the raw data that you send us. Which means that from a model implementation point of view, it's infinitely more straightforward, because you just have to ingest the score.

And if you have a legacy system, and you can't put a score into your actual system, because so many of us run on legacy systems, that's okay, you can still work with those scores within your collections environment, provided, obviously, you bring your collections team on the journey with you. And they understand what the scores are telling them. What will it mean to you and your organisation and your subsequent financials, if you can have those care conversations, customer service oriented conversations much earlier. And that's what we aim to do with scores for so we provide the score. And then you can use that in your strategies to better have the right conversations with the right people at the right time.

Brendan Le Grange 27:59

Training, consulting straight up scorecard building in this case, but you're also doing some writing you sharing your knowledge in just about every way possible. I got my copy of it's a risky business, a field guide to retail consumer credit in the UK from Amazon. So it's available widely. What does that book cover?

Carolyn Rohm 28:19

So Amazon is probably a really good bet, because it's on all the different Amazon flavours. It isn't in hardcopy in book shops, because funnily enough, it's a really niche audience. What does it cover? It covers pretty much everything that you need to understand from you're not a retail consumer credit risk analyst and you want to be a good one.

So we start with, let me grab my book here, we start with basic analytics. So how do we import data? What does that mean? We talk about exploratory data analysis, which if you've done any data science courses, as EDA visualisation, really popular data validations. And these things are often seen as quite dull and quite boring, massively important, if you want to be a good analyst, I hate to break to you have to do that stuff, and you have to do it well.

And then I get into the fundamentals of retail consumer credit risk. So what do we consider a policy rule? So a policy rule would be we will not lend to people under the age of 18, because they legally cannot sign a contract. Whereas a business rule is something that maybe you would override. So we won't lend to people under the age of 25. More of a business rule. So the policy rule is less than 18. Under 25. We might not want to allow a 22 year old to buy a Ferrari on credit. When we talk about using scorecards implementing scores.

We talk about concepts that are really critical, like tilting by risk, so you accelerate actions or conversations. So in originations world, you may confirm more details for high risk customers. Some of the other things we look at is what are observation windows and what are performance windows which is used a lot in scorecard modelling but it's also used in analytics. I talk about breaking scorecards, I remember being interviewed for a role. And I said, Yes, and one of the things we do is we break a scorecard and the CEO got really upset with me, because I'm not sure what he thought I meant.

But in credit risk, what we actually want to do is we do you want to push those limits where people are seen as low risk, we want to take fewer actions, because they save us, which means that that resource can then be applied to higher risk customers. So what you end up seeing is slightly worse behaviour in your good risk customers, better behaviour in your higher risk customers, which means that your scorecard doesn't differentiate as well as it did when it started.

And that's what I mean by breaking scorecards, things like transition matrices, we go into Markov chains, and how to predict gross charge off. So I really try and give a good flavour of working through credit risk and credit risk analytics, not scorecard building. I didn't go into scorecard building at all.

But I wanted to say, as an analyst, if you want to add value, from a credit risk point of view, these are the kinds of things you want to look at. This is why it's important. This is how you do it. This is what it is. And what can you share with your team, your organisation? And what impact can you have?

Brendan Le Grange 31:19

Yeah, and I think that subtitle of a field guide is really informative there, because it's written for the practical use. It's not an academic textbook, that looks at all the theories and goes deep into topics that you maybe would study at university. This is for somebody who's working in the field, who wants to do their job better. So yeah, real field guides you want to have in your hand.

And so yeah, everyone can head over to Amazon, to look for that.

Carolyn, you also, well, you've got your training, and you're speaking to other writing as well. So if people listening would like to learn more about that side of your work, keep in touch with you follow any projects you're rolling out? Where can they go online to find you, and to learn about some of those businesses.

Carolyn Rohm 32:06

So probably one of the places you can find out about me as LinkedIn, I'm definitely there. As we mentioned, I've got my website www.carolynrohm.com I'm a little bit on substack. I tend to talk about my summer off swimming. So it's not really work related, but if you're interested in long distance swimming, sometimes I write about that.

Yeah, those are most of the cases, you can find me a little bit on medium. Long distance swimming and underwater hockey, are my two passions,

Brendan Le Grange 32:32

a sport we have not featured on the show before I have to say. But yeah, Carolyn, I think you've covered some really great topics for this audience. In particular, we do talk from time to time with people, they get deep into things like machine learning, and AI and those tools.

And you know, we've talked to scorecard builders and how they build scores. And all of those are incredibly valuable for organisations, but they've got to be put together as well. And I think that to have the sort of services to grow your teams, to grow yourself is important to understand how to grow to take on the next role is important to have that book at your side. It's important.

So really, for people listening here, he said, it's not a big niche, but for us in the credit risk space, many ways that you can help. So I certainly recommend everyone go look for you on LinkedIn or www.carolynrohm.com. It's worth it if you've got a team to think about what you're doing for your staff to build those sorts of capabilities in them as well, which I remind everyone is available anywhere in the world online. So thank you so much for making the time to join me today. It's been a treat.

Carolyn Rohm 33:40

Thank you so much for having me. It's been lovely

Brendan Le Grange 33:41

And thank you all for listening.

Please do look for and follow the show on your favourite podcast platform and share the updates widely on LinkedIn where lending nerds are found in our largest concentration. Plus, send me a connection request while you're there.

This show is written and recorded by myself Brendan le Grange in Brighton, England and edited by Fina Charleson of FC Productions.

Show music is by Iam_wake, and you can find show notes and written transcripts at www.HowtoLendMoneytoStrangers.show and I'll see you again next Thursday.