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description=Richie and Jamie explore decision intelligence beyond BI, entity resolution across siloed data, building context graphs for fraud, AML, graph-RAG for LLMs to cut hallucinations, human-in-the-loop workflows, and ways to start today, and much more.;

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jamie hudsons in my system but i believe they re one and on each of those records i ve got two addresses actually if you ve got that inconsistency you could try and work out what the latest information is do it centrally you could get your customer relationship manager to ask the customer but you could also send out something that says we need you to revalidate your address please just provide this information in a more self serve automated way there are pros and cons with all of these the latter is probably the least intensive for the end for the bank or the for the customer for who s dealing with the data but for the end user there s they might be getting loads of these requests you ve gotta balance this with how much you re interfering with the customer flow versus you could have done centrally without even touching customers so these are the balances that people have to trade off richie cotton okay yeah and we certainly have definitely seen on some apps where oh we re not sure we ve got your details right can you just check that these are all up to date and all that sort of thing okay that makes sense and do you have a sense of what the sort of benefits are gonna be once you resolve this have you got a sense of like how much you can quantify like some sort of uplift from having the better systems in place here jamie hutton yeah look the thing about this is it s fundamental to the businesses that we work with if i take there was one organization who of course on this i won t be able to name who had they believed million customers approximately when we ran entity resolution we realized that was only about million real world entities people and businesses that s quite a delta isn t it that s yeah richie cotton like million made up customers that s gonna be a shock to your investors jamie hutton exactly so this is the sort of thing so then now you take that and you feed it into all your business processes you can treat those customers more holistically because previously they might have had a mortgage product over here a banking product over here or wealth product over here you can now treat them as one so clearly that s better customer experience better customer service and you can make a better risk decision are based on that customer cause you know everything about them so you ve got all of those benefits right through to as i mentioned before finding the bad stuff as well if i ve got a good view of who jamie is and every touch point that he has you can really unlock some huge benefits and some of the examples i ll give in the past we ve had systems where previously people have been trying to make a decision based on one record at a time or just basic information about the customer and they ve moved it to an entity or graph i ll talk about graph in a minute maybe level and they ve doubled the accuracy of their underlying decisioning models just by feeding in better context cause if you know who i am and who i relate to you can ultimately make a better decision richie cotton okay that that s pretty amazing and i can see how it s very easy for businesses or managers to like lie to themselves about what the stake of the business is if they don t have the right data about the customers about their all their finances and the rest of these things okay oh go on since you mentioned graphs graphs are like the technical topic at the moment talk me through what how d you make use of graphs in this case jamie hutton for sure so i guess our first part of it is you can t really have a good graph unless you ve done that entity resolution upfront if i ve got three customers that are all different customer ids jamie james and jim and i dunno they re the same there are different nodes in my graph right so the first thing you have to do to get a sensible and meaningful graph is to do entity resolution once once you ve got that what you tend to find richie is you ve probably heard of the six degrees of separation or six degrees of kevin bacon sometimes people say it this is really true in real world data and actually i d argue it s a lot less than six in most of the data that we see and this is everything connects to everything take a an example of let s take insurance just for a simple example how many customers have had their cars repaired by auto glass loads right because they ve had windscreen repairs is that an interesting link no if you think about banking how many of your customers have paid the local tax authority to pay their taxes or utility bill provider the answer is millions is that interesting not really so what you have to do is you take that massive graph that comes out of entity resolution you need to analyze it you need to break it up and understand which links are strong and which are just coincidental and that s ultimately what we do so we can analyze graphs in a couple of different ways one is you can build a graph around every entity in the system you could say here s jamie s graph here s qu limited graph what we re doing is we re taking those starting points and essentially just stepping out but following the strong links not the coincidental ones i mentioned earlier so that s a really classical technique when you re looking at graph analytics and then the other approach is you turn the problem on its head you don t have a starting point but you have a pattern you re looking for and looking for this sort of cyclical flow of funds or i m looking for a corporate ownership structure that looks a bit like this so i dunno where the starting point is i don t even know if i ve got any examples of this in my data the system goes away and finds all the relevant examples that match the criteria you re looking for so you can do it in those sort of two ways we describe it as egocentric starting at a particular start or more pattern based where you re looking across the whole system and ultimately by leveraging graph analytics why do we do this we do it to make better decisions if you can understand who is jamie who does jamie know then you ve got a much better idea of whether jamie s risky or has a new opportunity that you should be going after richie cotton absolutely and i ve seen some examples of cool analysis where you just look at who s been contacting who in terms of like phone calls or messages or whatever for like crime syndicates and you work out like who s at the top just cause of this like graph is is wild the kind of answers you can get outta things that you wouldn t expect just from connections between things rather than any real information a jamie hutton hundred percent if i talk about one of the use cases we do a lot of work with government we actually were the system behind the covid loans fraud detection so the home office use quanta to find where essentially back in the covid days people took out lots of loans with the intention of never paying them back often using shell companies if you re familiar with that companies that basically don t do anything they didn t do anything suddenly they get a loan and guess what the money never comes back and quanta was ultimately the graph technology underpinning that to say it looks like this it isn t just one person who s taken a loan and run away with it you ve got a an organized criminal gang a criminal network connected together taking out these loans and ultimately not paying them back yeah great example use case we ve got lots of sort of governmental use cases as well as the sort of commercial that i ve talked about earlier richie cotton okay i feel like i m learning a lot about how to do crime in this episode actually maybe beyond that are there any kind of other sorts of business data that you might want to store in a graph format jamie hutton yeah so when we think about data again you ll love obviously if you re a commercial organization you re talking about your customers you re talking about the relationships they have to other data so in in banking it d be payments in insurance it would be the claimants who you are claiming with just taking those examples so all of that data goes in but you generally want to augment it with third party data so the most common sources you would bring in things like corporate registry companies house in the uk or dun bradstreet in the us these are great examples of data sources which can add some significant value if you think about it if you want to establish how did jamie make money how did an individual make their money often most of the time it s through the businesses that they re associated with right so having an understanding of that helps you understand source of wealth but also if you think about it from a bad perspective if you wanted to launder loads of money and get loads of money through the system you re probably unlikely to do that on your retail bank account you re probably gonna use some kind of corporate entity to achieve that bringing in that sort of data can help both the positive and the negative really quite well and then you ll always have other sources that are gonna be relevant to specific use cases like when we re doing financial crime detection there are watch lists there are hot lists when you re doing things like compliance you want to have the politically exposed persons list so you understand relationships to political parties these are the sorts of extra data sources and each use case will have its own set of data if that makes sense that s relevant richie cotton that s really interesting and i haven t really thought about the idea of politically exposed people this is like people who are gonna be blackmailed or whatever and you want that to go into your crime risk list jamie hutton also politically i suppose people there s just certain extra handling that that a bank needs to do or a an organization needs to do once they know they have a pep so in some senses doesn t mean that they re risky but they do need to handle them differently so actually being able to just simply match to that list and know the exposure is one of the key parts of it richie cotton that s fascinating stuff and alright we talked about the nc resolutions is like working out how you go from the poor quality data to good quality data we ve got the context graph which is gonna go from we ve got data to this is gonna provide something useful what s the final step to getting some analytical result there to we can make a decision jamie hutton how do you serve that last step that s right so that s where i guess di that s the decision piece right so why do we do all of what i ve just described why do we resolve the end season build the grass we do it so that we can make better decisions and so what we re doing in the decision making piece we re applying just standard analytical tools and techniques this will be everything from the most boring thing which would be simple rules right through to complex deep learning models it can be anything in between right all different types of flavors of analytics and we try to be agnostic right we use the right pro tool for the job and there s no one size fits all for this stuff just taking simple example deep learning great but black box you cannot use it in compliance and regulated use cases because you have to explain how the model works so there are different models that will work for different different use cases what do we do we put better data into the models it really is simple as that richie if you can put a view of who is richie and who does richie connect to and know within the data and you feed that into the model s got more to go on it fundamentally understands it has a model of the real world the context graph is a model of the real world it s people connected to businesses connected to addresses connected to phone numbers it s a model of the real world so if you can feed that into the analytical model it just has so much more data to go on and it can make a much richer decision so if i give you a couple of examples if i was looking for risk if i find a high density of risk on a graph like i ve got people all connected together and lots of them are scoring for lots of risky reasons then that network that highly likely everyone on that network is has got a high an elevated set of risk greg s another great example in the risk space if you have a card or a a bank account which you don t do normal transactions on you re not shopping you re not getting your amazon you re not paying utility bills you re not doing anything normal that s okay people could have multiple cards but if you have a network of people and not a single one of them is doing anything normal that s probably not a real network that s probably a fake network in order to get banking products and potentially do money laundering or fraud so just giving that example and then flip it on the other side we do a lot of work around uncovering new business opportunities so if you already imagine have a banking relationship where qu are limited but your wealth division doesn t have any associations with the major shareholders of that company and you can see qu is a highly performing business then that gives you a nice warm lead doesn t it cause you can con contact your existing customer contact and say hey we d love to offer some services to your shareholders and the it works in the exact reverse if you ve got a wealth relationship with a particular individual and they suddenly spin up a new company surely you should be offering them banking services so you can see how you can use that same context graph to empower a decision on the bad side as well as a good side if that makes sense richie cotton oh wow it sounds like then this is gonna generate lots of opportunities for cross selling upselling you can expand your business more easily that way so it s a growth engine in that sense is that right jamie hutton exactly it could be used either way find a bad fun good richie cotton okay alright and a lot of the stuff you seem to be talking about sounds a bit like context engineering which is one of the hot topics in ai at the moment is providing more context for the models in order to get better answers do you wanna talk me through the relationship and how it feeds into ai use cases jamie hutton yeah love to and this is i guess where over the last few years things have changed so dramatically llms are unbelievably powerful at understanding the data that they were trained on which is all the publicly available data but of course what they won t have outta the box is access to your proprietary internal data for obvious reasons so what a loss of systems did a year two years ago maybe was they did this rag where basically all the l and m s doing is querying out to a set of data and pulling back the relevant records into its context window so that it can hopefully make some kind of decision and that works to an extent but i suppose what we do is we flip the problem slightly on its head rather than the llm calling out to differen...