Name: Owl.co: Identifying banks' customers
Uploaded: 2021-07-30T11:35:22.831Z
Duration: 434.36
Description: owl.co offers automated solutions for financial institutions to better know their customers by aggregating external data and delivering insights in real-time.

Ben Mullins - owl.co Demo (2019).mp4

owl.vtt

English

{{00:00:11}}
Good morning everyone.

{{00:00:13}}
Let's talk about a common name in the US actually the most
common name John, there are 5 million Johns in the US alone.

{{00:00:23}}
Now let's talk about a very common family
name, wilson there are 1 million Wilson.

{{00:00:30}}
In the US and in that statistic, we actually
included the ball from the movie cast away.

{{00:00:37}}
Okay, so John and Wilson now put them together- John
Wilson, you still have 15,000 people with John Wilson name

{{00:00:49}}
in the US so now imagine John Wilson walks into your bank
and wants to book and open a bank account as part of the

{{00:00:56}}
regulatory requirements, you have to do background checks.

{{00:01:00}}
This is a part of KYC and AML check.

{{00:01:02}}
So your analysts have to go through hundreds of different
databases and each of those databases probably returned 50,000

{{00:01:09}}
articles or 50,000 outputs because you have 15,000 of them and
then they have to go through each one and single one of them to

{{00:01:16}}
figure out if that's the right John Wilson to look at or not,
this takes so much time it's ruining the customer experience

{{00:01:26}}
and costing banks a hundred to 500 millions of dollars.

{{00:01:30}}
You think I'm exaggerating that you would say probably
banks have a streamlined is, but no it's painfully manual.

{{00:01:36}}
It takes two hours on average and for the case
of John Wilson, maybe it takes up to 10 hours.

{{00:01:42}}
We have actually done this with, for one of the top three largest
stock exchanges in north America, where we actually reduce

{{00:01:50}}
that time from three to four hours to less than 15 minutes.

{{00:01:55}}
Now, what if I tell you that we will be
able to uniquely identify this John Wilson?

{{00:02:00}}
Yes.

{{00:02:01}}
With like building a 360 view of him.

{{00:02:06}}
Yes.

{{00:02:07}}
And at the same time, be able to do that with just click of a button.

{{00:02:11}}
Hallelujah.

{{00:02:13}}
I'm Vahid I'm the CEO of Owl and here on stage with me is Sean my
co-founder CEO of Owl who's going to show you how we do just that.

{{00:02:22}}
Awesome.

{{00:02:22}}
So the subject in Vahid's speech is John Wilson
and John Wilson there's a little plot twist.

{{00:02:29}}
He's actually been convicted of a crime,
so we're going to put them on full blast.

{{00:02:32}}
What we're going to do is we're going to log in to
our plan and up here you can actually add entities.

{{00:02:38}}
Entities could be corporations, or it could be individuals,
but for the sake of this demo, we're going to do John Wilson

{{00:02:45}}
and the country that we're going to put is the United States.

{{00:02:48}}
We know that he's in Massachusetts and we
only need one other, a unique identifier.

{{00:02:53}}
It could be an email, it could be a phone number or for in
this case, we're just going to put, we know that at some

{{00:02:58}}
point many years ago he worked at Staples and that's it.

{{00:03:01}}
So we saved this entity, then you can run any kind of procedure.

{{00:03:05}}
On this individual, you can run, watch list, adverse media.

{{00:03:08}}
We have a full suite and it's fully configurable on the
backend, but we're going to run a full risk assessment.

{{00:03:17}}
So John Wilson, we find them, we run the assessment
and before any search goes out this is very critical.

{{00:03:23}}
We have to uniquely identify, it's a very common name.

{{00:03:25}}
We have to find the right John Wilson.

{{00:03:27}}
So we find every single Elliot that this individual
has we know that he's he goes by John, John B.

{{00:03:33}}
Wilson, Johnny Wilson, John Benedick Wilson.

{{00:03:36}}
Then we find all the geo locations that this
person either lives works or has worked and any

{{00:03:41}}
other properties he might own in different areas.

{{00:03:44}}
You see here, we found that at some point
he worked in Amsterdam in Netherlands.

{{00:03:49}}
So what, what the system does automatically, it
just starts, it starts looking to sanctions, watch

{{00:03:54}}
lists, regulatory databases relating to Netherlands.

{{00:03:59}}
Then we find all his previous job positions, we know which company
he worked at, what his position was, what year he worked there and

{{00:04:05}}
this is critical because if you want to run UBO's and you want to
with the regulations changing, this is going to be a very critical.

{{00:04:12}}
We also collect all his emails, phone numbers, personal
and work in the past 10 years, anything that he might have.

{{00:04:18}}
All this gets searched, and we want to look for inconsistent behavior.

{{00:04:22}}
Now we've fully uniquely identified this individual.

{{00:04:25}}
We know that that's the right John
Wilson and now the search has gone out.

{{00:04:28}}
So now we want to take a look, see if he has any risks.

{{00:04:31}}
See our system already says it's, this person is a
hundred percent problematic, which is why we chose him.

{{00:04:37}}
So if you take a look at why he's problematic, we
immediately find that he's been charged for conspiracy fraud,

{{00:04:44}}
bribery, money, laundry, and everything else basically.

{{00:04:47}}
He was one of the 50 individuals facing the charges in the
case, and this is the popular college scandal that just

{{00:04:53}}
happened on top of this we're also doing news synthesis.

{{00:04:58}}
We can find UCR system has, has decided that two of the news is
critical for me to review and we're doing sentiment analysis.

{{00:05:05}}
We figure out if the article is derogatory, we figure if that entity
has been portrayed positively or negatively in that news database.

{{00:05:13}}
Back to you.

{{00:05:13}}
Right.

{{00:05:14}}
Thank you.

{{00:05:15}}
So the magic that you saw in the, in that five
minutes is something that takes hours for banks to, to

{{00:05:22}}
actually do and be in that five minutes, we actually
flagged that this bank account should not be open.

{{00:05:27}}
So just to recap and tell you what, what makes us unique?

{{00:05:32}}
AKA awesome.

{{00:05:33}}
One the fact that the uniquely identified this individual,
we know the series has lived in his age and all the

{{00:05:39}}
job history of of the individual we find much more.

{{00:05:43}}
At the same time, the second piece, which is all the databases
that you saw in our demo, you can easily add or remove to

{{00:05:49}}
those databases to comply with your specific requirements
and we can even vary that by the jurisdiction that you're in.

{{00:05:57}}
Lastly, the fact that we are zero knowledge, we do not see your
store any of your customer data is fully entwined encrypted.

{{00:06:04}}
So now if you're a bank that these types of
checks is crippling your growth and scaling up.

{{00:06:10}}
Please reach out and go to this website, book a demo, and
we can actually show you more because we can use the same

{{00:06:15}}
data fragments for personalization of the experience during
onboarding and also we can use the same for fraud detection.

{{00:06:22}}
Lastly, if you're an investor in the room, probably this
part is interesting for you is that the only 15 months

{{00:06:27}}
into the market, we managed to be working with 10 of the
largest banks and insurance companies in North America.

{{00:06:35}}
That growth, we basically closed our seed round a
couple of months back $2.6 million and we reached our

{{00:06:42}}
revenue targets this year, halfway through the year.

{{00:06:45}}
So we are ramping up for series a.

{{00:06:46}}
So if you think your dollar is biggest strategic value,
please stop by our booth and we would love to chat.

{{00:06:56}}
Thank you.

Let's talk about a common name in the US actually the most
common name John, there are 5 million Johns in the US alone.

Now let's talk about a very common family
name, wilson there are 1 million Wilson.

In the US and in that statistic, we actually
included the ball from the movie cast away.

Okay, so John and Wilson now put them together- John
Wilson, you still have 15,000 people with John Wilson name

in the US so now imagine John Wilson walks into your bank
and wants to book and open a bank account as part of the

regulatory requirements, you have to do background checks.

So your analysts have to go through hundreds of different
databases and each of those databases probably returned 50,000

articles or 50,000 outputs because you have 15,000 of them and
then they have to go through each one and single one of them to

figure out if that's the right John Wilson to look at or not,
this takes so much time it's ruining the customer experience

and costing banks a hundred to 500 millions of dollars.

You think I'm exaggerating that you would say probably
banks have a streamlined is, but no it's painfully manual.

It takes two hours on average and for the case
of John Wilson, maybe it takes up to 10 hours.

We have actually done this with, for one of the top three largest
stock exchanges in north America, where we actually reduce

that time from three to four hours to less than 15 minutes.

Now, what if I tell you that we will be
able to uniquely identify this John Wilson?

And at the same time, be able to do that with just click of a button.

I'm Vahid I'm the CEO of Owl and here on stage with me is Sean my
co-founder CEO of Owl who's going to show you how we do just that.

So the subject in Vahid's speech is John Wilson
and John Wilson there's a little plot twist.

He's actually been convicted of a crime,
so we're going to put them on full blast.

What we're going to do is we're going to log in to
our plan and up here you can actually add entities.

Entities could be corporations, or it could be individuals,
but for the sake of this demo, we're going to do John Wilson

and the country that we're going to put is the United States.

We know that he's in Massachusetts and we
only need one other, a unique identifier.

It could be an email, it could be a phone number or for in
this case, we're just going to put, we know that at some

point many years ago he worked at Staples and that's it.

So we saved this entity, then you can run any kind of procedure.

On this individual, you can run, watch list, adverse media.

We have a full suite and it's fully configurable on the
backend, but we're going to run a full risk assessment.

So John Wilson, we find them, we run the assessment
and before any search goes out this is very critical.

We have to uniquely identify, it's a very common name.

So we find every single Elliot that this individual
has we know that he's he goes by John, John B.

Wilson, Johnny Wilson, John Benedick Wilson.

Then we find all the geo locations that this
person either lives works or has worked and any

other properties he might own in different areas.

You see here, we found that at some point
he worked in Amsterdam in Netherlands.

So what, what the system does automatically, it
just starts, it starts looking to sanctions, watch

lists, regulatory databases relating to Netherlands.

Then we find all his previous job positions, we know which company
he worked at, what his position was, what year he worked there and

this is critical because if you want to run UBO's and you want to
with the regulations changing, this is going to be a very critical.

We also collect all his emails, phone numbers, personal
and work in the past 10 years, anything that he might have.

All this gets searched, and we want to look for inconsistent behavior.

Now we've fully uniquely identified this individual.

We know that that's the right John
Wilson and now the search has gone out.

So now we want to take a look, see if he has any risks.

See our system already says it's, this person is a
hundred percent problematic, which is why we chose him.

So if you take a look at why he's problematic, we
immediately find that he's been charged for conspiracy fraud,

bribery, money, laundry, and everything else basically.

He was one of the 50 individuals facing the charges in the
case, and this is the popular college scandal that just

happened on top of this we're also doing news synthesis.

We can find UCR system has, has decided that two of the news is
critical for me to review and we're doing sentiment analysis.

We figure out if the article is derogatory, we figure if that entity
has been portrayed positively or negatively in that news database.

So the magic that you saw in the, in that five
minutes is something that takes hours for banks to, to

actually do and be in that five minutes, we actually
flagged that this bank account should not be open.

So just to recap and tell you what, what makes us unique?

One the fact that the uniquely identified this individual,
we know the series has lived in his age and all the

job history of of the individual we find much more.

At the same time, the second piece, which is all the databases
that you saw in our demo, you can easily add or remove to

those databases to comply with your specific requirements
and we can even vary that by the jurisdiction that you're in.

Lastly, the fact that we are zero knowledge, we do not see your
store any of your customer data is fully entwined encrypted.

So now if you're a bank that these types of
checks is crippling your growth and scaling up.

Please reach out and go to this website, book a demo, and
we can actually show you more because we can use the same

data fragments for personalization of the experience during
onboarding and also we can use the same for fraud detection.

Lastly, if you're an investor in the room, probably this
part is interesting for you is that the only 15 months

into the market, we managed to be working with 10 of the
largest banks and insurance companies in North America.

That growth, we basically closed our seed round a
couple of months back $2.6 million and we reached our

revenue targets this year, halfway through the year.

So if you think your dollar is biggest strategic value,
please stop by our booth and we would love to chat.

owl.co offers automated solutions for financial institutions to better know their customers by aggregating external data and delivering insights in real-time. 