Join David Knibbe, CEO of NN Group and Kevin McLoughlin, Partner & Co-Founder of MTech Capital as they dive into the role that technology has played in transforming NN into a major insurance player.

Transcript

0:01

[Music]

0:10

david welcome to this itc fireside chat

0:14

today we’re going to be discussing the

0:16

role of technology in the transformation

0:18

of nn group

0:21

first let me start by introducing us

0:23

briefly

0:24

david knieby is ceo of nn group

0:29

nn is a multi-line insurer based in the

0:31

netherlands

0:32

it has leading market positions in

0:35

corporate pensions

0:37

life and pnc

0:39

the group includes the number five bank

0:41

in the netherlands

0:43

uh leading businesses in life and

0:45

pensions across central and eastern

0:47

europe

0:48

and

0:49

japan

0:51

and my name is kevin mclaughlin

0:53

i’m partner and co-founder of mtech

0:55

capital we’re a vc firm focused

0:58

exclusively on insure tech including

1:01

asset management

1:03

we invest across north america

1:06

europe including israel

1:08

and we have offices in santa monica

1:11

california and london

1:15

david

1:16

last month you had a capital markets day

1:19

where you refreshed and reconfirmed

1:23

the strategic goals operational

1:25

objectives of an end group

1:28

so this interview is perfectly timed

1:31

um

1:32

if i can sum up that strategy and a

1:35

phrase

1:38

your goal is for nn to become a

1:40

customer-centric and data-driven company

1:44

uh what role will technology play in

1:46

this transformation

1:48

yeah well hello uh kevin and

1:51

and hello everybody very good to be here

1:53

first of all

1:54

um yeah indeed we just launched our

1:57

uh our new strategy uh which we launched

1:59

at our at our capital markets day it is

2:01

a strategy around creating value for for

2:04

all stakeholders

2:05

but the content of the strategy indeed i

2:07

think you summarize very well it’s very

2:09

much about being a customer-centric

2:11

organization and and very data-driven um

2:15

as well now clearly technology i mean

2:18

it’s obvious will play a crucial role uh

2:21

in that and maybe it’s helpful if i give

2:23

some examples i think part of the

2:25

strategy for example what we’re doing is

2:26

that we want to make sure that as an

2:28

insurance company we are really at the

2:30

at the forefront of our interactions

2:32

with customers i think at the end of the

2:34

day as an insurance company you need to

2:36

decide where you want to be in the value

2:37

chain and we want to be very much in the

2:39

forefront of our of all the interactions

2:41

with customers but if you want to do

2:43

that you have to understand your

2:44

customers you need to know them well and

2:46

then of course data and technology plays

2:48

a crucial role

2:50

in order to be able to know what’s going

2:52

on with our customers and how we can uh

2:53

can service them best

2:55

um so that’s an example where technology

2:57

is crucial

2:59

another example in our strategies of

3:00

course is platforms and and i’m sure

3:02

people talk a lot about it

3:04

we are very active uh particularly in

3:07

workforce management and carefree

3:08

retirement and again technology plays a

3:11

plays a crucial role in all of these all

3:13

of these new developments so i believe

3:15

in three years we’ll have a

3:16

fundamentally different company but a

3:19

lot more data and technology driven than

3:21

than today

3:23

and when you think about uh the

3:26

near-term priorities for transforming nn

3:29

with technology what is what is your

3:32

emphasis

3:33

is it you know life pensions pnc or in

3:36

terms of the value chain is it

3:38

distribution

3:39

you know uh policy administration

3:41

underwriting claims

3:44

yeah i know i think there’s there’s

3:45

indeed many topics that uh as a as a

3:48

broad company as a multi-line company

3:49

that uh that we are that we look at but

3:52

one important element for example is

3:54

scale

3:55

uh we have recently required delta lloyd

3:57

uh in the netherlands and uh

3:59

an even more recent fifa which made his

4:01

market leader in in both life and

4:03

non-life now to be able to to really

4:05

let’s say cash in on that scale

4:08

technology plays a crucial role if we’re

4:09

not aligning our platforms if we’re not

4:11

bringing our customers all to one

4:13

platform and if we don’t use all the

4:15

data that we’re getting to the best way

4:17

possible we’re not really cashing in on

4:19

on the scale so that is a

4:22

very important element another important

4:24

element is our core business if you will

4:26

especially in the international markets

4:28

is around life protection

4:30

um

4:31

now clearly underwriting and data and

4:34

and pricing and underwriting become more

4:36

and more important more and more is

4:38

possible risk selection i mean i don’t

4:40

think we’re at the level where we can

4:42

price each risk individually for each

4:44

customer but uh we’re clearly on our way

4:47

in that uh in that direction and again

4:49

data and technology uh are an important

4:52

part there uh and the more we can

4:54

separate ourselves in terms of our

4:56

underwriting capabilities

4:58

the better so so these are important

5:01

elements uh

5:02

that are part of our strategy where we

5:04

want to create long-term value

5:07

and these are elements that are crucial

5:09

for the coming years to be successful

5:13

and just

5:14

staying for a minute on this topic of

5:16

where with an nn group you see the scope

5:19

for greatest value creation and

5:21

transformation by technology at mtec

5:23

capital for example

5:25

um we see

5:27

50 startups we meet with 50 startups per

5:30

month on average

5:32

the vast majority of these are focused

5:34

on pnc

5:36

so do you see kind of your your greater

5:39

scope for the adoption of technology in

5:41

the pnc side of your business than life

5:43

or not

5:44

yes it’s a good question the um and

5:47

indeed there is a a difference in in the

5:50

application of in the life and in the

5:51

non-life i i would argue that it’s not

5:54

the case that there

5:56

is more value on the non-life setting on

5:58

the live side it’s just more visible if

6:00

you look at a lot of the startups a lot

6:02

of is around uh you know these these

6:04

startups around whether it’s around

6:05

claim handling it’s about underwriting

6:07

and there’s a lot of front-end

6:08

applications and they tend to be

6:10

on the non-life side on the pnc side i

6:13

think also often because it’s easier to

6:15

get customer interaction on these type

6:17

of

6:18

products than on the on the live site

6:20

and uh but there’s clearly a lot of

6:22

value in it if i look at the live site

6:24

and we are a very large player in in

6:26

life insurance and in pensions um there

6:29

is enormous value however it’s often a

6:31

bit less sexy which is migration of

6:33

legacy platforms so we migrate

6:36

everything to new platforms with the

6:37

technology that we now have i remember

6:39

when i was running the live company

6:41

quite a while ago

6:42

i mean we would try to do conversions

6:44

and every weekend we would maybe get 60

6:46

70 000 policies and and we’ve now had

6:49

even remotely in those in these corona

6:51

days where we’ve been doing policy

6:53

migration from home

6:54

and literally a million policies go from

6:56

one platform uh uh to the other so the

6:59

technology there is very important

7:02

another element on the live side where i

7:03

see a big difference is we used to have

7:05

kind of a screen scraping where you know

7:08

you could have certain

7:10

amendments on policies you could do via

7:12

screen scraping but every time something

7:14

in the process changed you know the

7:16

thing didn’t work anymore and now we

7:17

have robots that in a way essentially

7:20

kind of learn and think that like some

7:22

of our employees do and say okay maybe

7:24

this this cell moved a bit but i

7:26

understand what the purpose is so i’ll

7:28

i’ll adapt it myself

7:30

so it’s a lot of applications also on

7:32

the live side that create a lot of value

7:34

it’s just a bit less visible maybe it’s

7:36

potentially maybe a bit less less sexy

7:38

but in terms of value creation i

7:40

certainly wouldn’t underestimate it then

7:44

and kind of

7:46

we can focus on life if you wish but

7:48

just when you think about

7:50

where you’ve introduced technology

7:52

already where do you see kind of the

7:54

greatest where have you seen the

7:56

greatest benefits already and what has

7:58

not worked

8:00

yeah so i think we’ve seen a lot of

8:02

benefit already on the customer side uh

8:04

let me take for example our home market

8:05

as an example we now have close to seven

8:08

million customers uh which is you know

8:11

very substantial in in a market of 17

8:13

million uh 17 million people um they’re

8:17

all in one database and one of the

8:18

things that we’ve done is build an

8:20

engine around next best actions so

8:22

basically based on everything that we

8:24

know enriched with external data

8:27

and peer comparisons uh we’re getting

8:29

better and better at predicting towards

8:31

our customers what is the most logical

8:34

next step that this customer takes and

8:36

people often immediately think ah that’s

8:38

that’s cross seller deep sell but a lot

8:39

of it is actually servicing it could be

8:42

your car is overinsured maybe

8:44

the car is still all risk insured and

8:46

given you know the age of the car you

8:48

know you could have a a more simple

8:50

coverage uh maybe people don’t realize

8:53

that their children are becoming either

8:54

18 or they’re going to college and and

8:56

some things are changing in their uh in

8:58

their policies so a lot is also around

9:00

servicing where we’ve gotten much better

9:02

at it if we get it right whether it’s

9:04

via chat via it’s where they’re

9:07

calls so all the only channel

9:09

servicing that we do where we’re getting

9:11

better at is getting the right offer to

9:13

the customer what we’ve seen certainly

9:15

in the beginning if we get it wrong i

9:17

mean customers are very harsh so if you

9:19

you come up with a suggestion that is

9:21

wrong

9:22

you see it immediately backfires in your

9:25

mps and you’re worse off versus doing

9:27

nothing and so the learning has been

9:29

that we better really know what we’re

9:31

doing because if we do it well we get a

9:33

plus in our net promoters scores

9:36

if we get it wrong you really get uh get

9:38

punished for it so there’s been some

9:40

real value in terms of customer loyalty

9:42

in terms of nps but it’s it’s a longer

9:44

road to

9:46

to get it right another example is uh

9:49

we’ve used a lot of technology and again

9:51

this is probably

9:52

uh yeah might sound less less sexy than

9:55

than some of the other applications is

9:57

around email classification so we

9:59

literally get emails uh every day and we

10:02

get a lot of emails in and in a large

10:04

company a multi-line you don’t always

10:07

exactly know where these emails will go

10:09

and and how to classify them the first

10:11

step we’ve been doing is like on a fully

10:13

automated basis you get immediately the

10:15

email to the right person in the company

10:18

um that was step one

10:20

took quite some time to get that right

10:22

now we’re actually getting even better

10:23

because now we can go into the email and

10:26

already based on on what is in the

10:28

emails the suggested answers uh

10:31

is now the next phase that that we’re

10:33

working on now the amount of efficiency

10:36

that you create by these type of simple

10:38

things are

10:40

in my view are enormous certainly if you

10:42

have a large inforce book like like we

10:44

do

10:46

excellent um

10:50

of course insurance as an industry has

10:52

always been

10:54

driven by data

10:56

data and data analytics

10:58

but of course with ai including machine

11:01

learning nlp ocr

11:04

um

11:05

we have

11:06

you know tools tools that we can work

11:09

with

11:10

right across the value chain and

11:11

insurance

11:12

what’s nn’s general approach for using

11:15

ai and data science

11:17

right right yeah so we i think we’ve

11:19

been at the trying to be at the

11:21

forefront of all the uses of ai and i

11:23

think we’ve probably made you know the

11:27

mistakes that everybody did uh um you

11:30

know you you’d hope you avoid them but i

11:32

think quite a few of the mistakes

11:35

that generally are made

11:37

uh we also made which was

11:39

we started also with a separate team

11:42

that was doing this with some very

11:44

bright people and were really good at it

11:46

and

11:47

in hindsight we spent a lot of time on

11:50

getting the data in order getting the

11:52

right technology

11:54

trying to work with the technology

11:56

spend quite some time on all the

11:58

algorithms and what works and what

12:00

doesn’t work and then usa use cases came

12:02

out and i think that’s a model that

12:05

to be honest really doesn’t work that

12:06

well uh because there’s a couple of

12:08

issues uh with it in hindsight and that

12:11

is what we have changed now

12:13

i think the technology you know it’s

12:15

there the algorithms i think most of it

12:17

was probably invented by by

12:18

mathematicians in the 60s so the real

12:21

challenge is not that the real challenge

12:23

is getting you know the business

12:24

application of it how do you get large

12:26

organizations to actually adapt to it

12:29

and incorporate it instead of either

12:32

fighting it or proving it they don’t

12:33

need it or proving that the existing

12:35

model is is better or saying well it’s

12:38

great that you have these 10 brilliant

12:40

people there but i’m today really too

12:42

busy because i’m already launching a

12:44

product or launching a new platform and

12:46

i really don’t have time to to spend on

12:48

these type of things and i think that’s

12:51

been the challenge that that we also had

12:53

we’ve really converted it and turned it

12:55

around now so uh the overall goal is to

12:58

spend at least 70 of our time on

13:00

actually on use cases and maybe only 20

13:03

of our time on the actual algorithms and

13:05

on the

13:06

let’s say on the technology

13:09

and then now we’re starting to see that

13:11

even though we started with a lot of

13:13

cross-sell and other activities and now

13:16

you see so the example of the emails or

13:18

the robots are typically things that the

13:20

operational people really really needed

13:22

they add a lot of value

13:25

and at the same time uh you know the the

13:27

people that are now working in the ai

13:29

and in the the data science you know

13:31

suddenly now get flooded with requests

13:33

because people starting to see the um

13:35

you know the added value but to be

13:36

honest it’s been a journey we started i

13:38

think more in the classical way but

13:40

because we started early i do think

13:41

we’re well on our way now to to really

13:43

benefit from it

13:45

and in just in maybe a word on

13:48

to the extent you have found the um

13:51

perhaps a a critical obstacle being

13:54

corporate culture and just receptivity

13:56

to technology what specifically did nn

13:59

do to address that

14:01

i think the uh i’m not going to say

14:04

we’re there because this will continue

14:05

to be a

14:06

a challenge and but i think a lot

14:10

uh it helps a lot is as a senior

14:13

management you spend time on so that you

14:15

spend time talking to these people but

14:17

also talking to let’s say the business

14:19

units and the people in operations and

14:21

making sure that you continue to talk

14:23

about it and make it important

14:25

uh i think that’s one topic the other

14:27

topic is it’s very tempting after the

14:29

first attempt that we had where use

14:31

cases were built and didn’t work to to

14:33

be overly critical on it and explain to

14:35

everybody why it didn’t work and uh

14:38

but you should really avoid in my view

14:40

uh the finger pointing and and try to

14:43

get uh over that point as quickly as

14:46

possible and then obviously build on

14:48

success you know the small examples that

14:50

we had in the

14:52

in the beginning um you know i tried to

14:55

use as much as possible in town halls

14:56

and i i try to take it away from you

14:59

know this department is doing better

15:00

than the other one but just to to in a

15:03

positive way reinforce everybody that

15:05

this is the

15:06

uh this is the way forward um i think

15:08

we’re making progress there but like any

15:11

anything new uh it’s also fair to say

15:14

that we have uh i still see a lot more

15:16

opportunities than than we do today i

15:18

mean we just started we’ve been

15:19

working with the model towards customers

15:22

and so lessen the operations more

15:24

towards customers

15:26

well talking to customers on some of the

15:28

analytics that you do given privacy

15:30

concerns data concerns is again a

15:33

separate expertise so now we’re looking

15:35

at how do you interact with customers

15:36

once you have all this this data and you

15:39

think you have added value for them so

15:40

it’s also for us it’s still a very much

15:42

a learning journey

15:45

excellent um one other question um

15:49

which all insure techs want to know the

15:51

answer to um

15:54

uh

15:56

most of the insured techs that we

15:57

encounter are not out to disrupt uh to

16:01

displace incumbent insurers but rather

16:04

actually to service them through

16:06

products or or

16:08

technology different products and

16:10

services

16:11

um

16:12

having said that it can be a challenge

16:14

for uh these companies to work with

16:16

large complex insurance groups uh how do

16:19

you approach

16:20

at nn working with uh startup companies

16:24

why is that by the way kevin why are

16:25

they not trying to disrupt us that’s uh

16:27

sounds like a lot more fun than trying

16:29

to service these large insurance

16:31

companies uh

16:33

the vast majority there are certainly

16:34

some out there looking to disrupt and i

16:36

would say um

16:39

let’s say i would say to enhance and

16:42

improve

16:43

aspects of your business

16:45

and then provide that to insurance

16:48

incumbents is what we see more of than

16:51

the very large business models you know

16:53

the full stack insurers or

16:56

who are uh are out to displace you

16:59

so i think it’s uh

17:00

it’s we see both

17:02

but the vast majority i think are in the

17:04

business of probably servicing insurance

17:07

incumbents

17:08

right all right all right yeah so i

17:10

think going back to your question yeah i

17:12

mean clearly i mean there’s many times

17:13

that we are jealous of these intro texts

17:15

but i think the amount of innovation and

17:18

agility and and creative thinking uh

17:21

original thinking is something that uh

17:24

you know i would like to see in an end

17:25

all the time and and uh you know

17:28

we don’t always have that at the level

17:30

that some of the insured techs have and

17:31

i think that is tremendously valuable at

17:34

the same time we you know in a large

17:36

company we have a strong brand we have

17:38

capital we have a lot of customers we

17:40

have a lot of interaction and that’s

17:41

something that that obviously is very

17:43

valuable for any company and also for um

17:46

for insurer tax

17:48

i think the experience so far has been

17:50

that we have been working a bit more

17:52

with scale-ups than with really startups

17:55

and the reason being is not so much that

17:57

the ideas

17:59

yeah that we see an insure tax or in

18:01

really the startups are not as good as

18:03

the scale ups but a lot has to do also

18:05

with the scale i mean we’re a fairly

18:07

large organization and we’ve seen that a

18:09

lot of the you know the applications and

18:12

the ideas that people have um

18:15

become more complex

18:17

you know they’ve tested it with a

18:18

hundred with a thousand but if you

18:20

suddenly test it with millions of

18:22

customers of lots of operational streams

18:24

uh things become a lot more complex and

18:28

there you see that on the one hand we

18:30

always want to build size as a company

18:31

and then we’ve been you know active in

18:33

that field too but at the same time this

18:35

size also creates a lot of complexity

18:37

and from that point of view our

18:38

experience with scale-ups is a bit

18:39

better not because their concepts are

18:41

always better but usually they’re a bit

18:42

better in dealing with the complexity

18:45

that large uh

18:47

large organizations have but we do have

18:49

uh we have quite a bit of partnerships

18:51

all over the place and it’s uh some of

18:53

it is uh

18:54

as an investment and some of it is uh um

18:58

you know because we already see some uh

19:00

possibility to apply it within our

19:02

organization um so we are actively

19:05

engaging and um

19:07

uh trying to figure out who we’re going

19:09

to try and work with and

19:11

or invest in

19:15

david kneebe ceo of nn group thank you

19:19

thank you pleasure to be here

19:28

you

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